Advances in Neural Information Processing Systems 34

NeurIPS 2021

Edited by M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan

ISBN: 9781713845393

2334 papers

Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning Christoph Dann, Teodor Vanislavov Marinov, Mehryar Mohri, Julian Zimmert
Learning One Representation to Optimize All Rewards Ahmed Touati, Yann Ollivier
Matrix factorisation and the interpretation of geodesic distance Nick Whiteley, Annie Gray, Patrick Rubin-Delanchy
UniDoc: Unified Pretraining Framework for Document Understanding Jiuxiang Gu, Jason Kuen, Vlad I Morariu, Handong Zhao, Rajiv Jain, Nikolaos Barmpalios, Ani Nenkova, Tong Sun
Finding Discriminative Filters for Specific Degradations in Blind Super-Resolution Liangbin Xie, Xintao Wang, Chao Dong, Zhongang Qi, Ying Shan
Counterfactual Explanations Can Be Manipulated Dylan Slack, Anna Hilgard, Himabindu Lakkaraju, Sameer Singh
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks Hengrui Zhang, Qitian Wu, Junchi Yan, David P. Wipf, Philip S Yu
BAST: Bayesian Additive Regression Spanning Trees for Complex Constrained Domain Zhao Tang Luo, Huiyan Sang, Bani Mallick
Hyperbolic Busemann Learning with Ideal Prototypes Mina Ghadimi Atigh, Martin Keller-Ressel, Pascal Mettes
Backward-Compatible Prediction Updates: A Probabilistic Approach Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler
Truncated Marginal Neural Ratio Estimation Benjamin K Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger
ReAct: Out-of-distribution Detection With Rectified Activations Yiyou Sun, Chuan Guo, Yixuan Li
Non-local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation Jogendra Nath Kundu, Siddharth Seth, Anirudh Jamkhandi, Pradyumna YM, Varun Jampani, Anirban Chakraborty, Venkatesh Babu R
Fast Training of Neural Lumigraph Representations using Meta Learning Alexander Bergman, Petr Kellnhofer, Gordon Wetzstein
Analytical Study of Momentum-Based Acceleration Methods in Paradigmatic High-Dimensional Non-Convex Problems Stefano Sarao Mannelli, Pierfrancesco Urbani
Multimodal Few-Shot Learning with Frozen Language Models Maria Tsimpoukelli, Jacob L Menick, Serkan Cabi, S. M. Ali Eslami, Oriol Vinyals, Felix Hill
Approximating the Permanent with Deep Rejection Sampling Juha Harviainen, Antti Röyskö, Mikko Koivisto
Revisiting Model Stitching to Compare Neural Representations Yamini Bansal, Preetum Nakkiran, Boaz Barak
AugMax: Adversarial Composition of Random Augmentations for Robust Training Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, Zhangyang Wang
Habitat 2.0: Training Home Assistants to Rearrange their Habitat Andrew Szot, Alexander Clegg, Eric Undersander, Erik Wijmans, Yili Zhao, John Turner, Noah Maestre, Mustafa Mukadam, Devendra Singh Chaplot, Oleksandr Maksymets, Aaron Gokaslan, Vladimír Vondruš, Sameer Dharur, Franziska Meier, Wojciech Galuba, Angel Chang, Zsolt Kira, Vladlen Koltun, Jitendra Malik, Manolis Savva, Dhruv Batra
Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods Seohong Park, Jaekyeom Kim, Gunhee Kim
Meta-Learning Reliable Priors in the Function Space Jonas Rothfuss, Dominique Heyn, jinfan Chen, Andreas Krause
VoiceMixer: Adversarial Voice Style Mixup Sang-Hoon Lee, Ji-Hoon Kim, Hyunseung Chung, Seong-Whan Lee
Predicting What You Already Know Helps: Provable Self-Supervised Learning Jason Lee, Qi Lei, Nikunj Saunshi, JIACHENG ZHUO
Oracle Complexity in Nonsmooth Nonconvex Optimization Guy Kornowski, Ohad Shamir
CentripetalText: An Efficient Text Instance Representation for Scene Text Detection Tao Sheng, Jie Chen, Zhouhui Lian
Learning to Select Exogenous Events for Marked Temporal Point Process Ping Zhang, Rishabh Iyer, Ashish Tendulkar, Gaurav Aggarwal, Abir De
DRIVE: One-bit Distributed Mean Estimation Shay Vargaftik, Ran Ben-Basat, Amit Portnoy, Gal Mendelson, Yaniv Ben-Itzhak, Michael Mitzenmacher
Learning Space Partitions for Path Planning Kevin Yang, Tianjun Zhang, Chris Cummins, Brandon Cui, Benoit Steiner, Linnan Wang, Joseph E Gonzalez, Dan Klein, Yuandong Tian
Progressive Feature Interaction Search for Deep Sparse Network Chen Gao, Yinfeng Li, Quanming Yao, Depeng Jin, Yong Li
Local Explanation of Dialogue Response Generation Yi-Lin Tuan, Connor Pryor, Wenhu Chen, Lise Getoor, William Yang Wang
Scalable Inference in SDEs by Direct Matching of the Fokker–Planck–Kolmogorov Equation Arno Solin, Ella Tamir, Prakhar Verma
The Complexity of Bayesian Network Learning: Revisiting the Superstructure Robert Ganian, Viktoriia Korchemna
Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation Kazu Ghalamkari, Mahito Sugiyama
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj
Numerical influence of ReLU’(0) on backpropagation David Bertoin, Jérôme Bolte, Sébastien Gerchinovitz, Edouard Pauwels
A Contrastive Learning Approach for Training Variational Autoencoder Priors Jyoti Aneja, Alex Schwing, Jan Kautz, Arash Vahdat
What training reveals about neural network complexity Andreas Loukas, Marinos Poiitis, Stefanie Jegelka
Class-agnostic Reconstruction of Dynamic Objects from Videos Zhongzheng Ren, Xiaoming Zhao, Alex Schwing
Unique sparse decomposition of low rank matrices Dian Jin, Xin Bing, Yuqian Zhang
Neighborhood Reconstructing Autoencoders Yonghyeon LEE, Hyeokjun Kwon, Frank Park
TopicNet: Semantic Graph-Guided Topic Discovery Zhibin Duan, Yi.shi Xu, Bo Chen, dongsheng wang, Chaojie Wang, Mingyuan Zhou
(Almost) Free Incentivized Exploration from Decentralized Learning Agents Chengshuai Shi, Haifeng Xu, Wei Xiong, Cong Shen
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, Christopher Ré
Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness Zifeng Wang, Tong Jian, Aria Masoomi, Stratis Ioannidis, Jennifer Dy
T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on Graphs Changwoo Lee, Zhao Tang Luo, Huiyan Sang
The Utility of Explainable AI in Ad Hoc Human-Machine Teaming Rohan Paleja, Muyleng Ghuy, Nadun Ranawaka Arachchige, Reed Jensen, Matthew Gombolay
Subgoal Search For Complex Reasoning Tasks Konrad Czechowski, Tomasz Odrzygóźdź, Marek Zbysiński, Michał Zawalski, Krzysztof Olejnik, Yuhuai Wu, Łukasz Kuciński, Piotr Miłoś
MCMC Variational Inference via Uncorrected Hamiltonian Annealing Tomas Geffner, Justin Domke
Landmark-RxR: Solving Vision-and-Language Navigation with Fine-Grained Alignment Supervision Keji He, Yan Huang, Qi Wu, Jianhua Yang, Dong An, Shuanglin Sima, Liang Wang
A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness James Diffenderfer, Brian Bartoldson, Shreya Chaganti, Jize Zhang, Bhavya Kailkhura
On the Importance of Gradients for Detecting Distributional Shifts in the Wild Rui Huang, Andrew Geng, Yixuan Li
Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods Terrance Liu, Giuseppe Vietri, Steven Z. Wu
Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization Clement Gehring, Kenji Kawaguchi, Jiaoyang Huang, Leslie P. Kaelbling
Mirror Langevin Monte Carlo: the Case Under Isoperimetry Qijia Jiang
Do Different Tracking Tasks Require Different Appearance Models? Zhongdao Wang, Hengshuang Zhao, Ya-Li Li, Shengjin Wang, Philip Torr, Luca Bertinetto
Towards robust vision by multi-task learning on monkey visual cortex Shahd Safarani, Arne Nix, Konstantin Willeke, Santiago Cadena, Kelli Restivo, George Denfield, Andreas Tolias, Fabian H. Sinz
Arbitrary Conditional Distributions with Energy Ryan Strauss, Junier B Oliva
Learning Domain Invariant Representations in Goal-conditioned Block MDPs Beining Han, Chongyi Zheng, Harris Chan, Keiran Paster, Michael Zhang, Jimmy Ba
Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning Scott Sussex, Caroline Uhler, Andreas Krause
Fuzzy Clustering with Similarity Queries Wasim Huleihel, Arya Mazumdar, Soumyabrata Pal
Improving black-box optimization in VAE latent space using decoder uncertainty Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal
Sample Selection for Fair and Robust Training Yuji Roh, Kangwook Lee, Steven Whang, Changho Suh
NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL Khaled Nakhleh, Santosh Ganji, Ping-Chun Hsieh, I-Hong Hou, Srinivas Shakkottai
Sageflow: Robust Federated Learning against Both Stragglers and Adversaries Jungwuk Park, Dong-Jun Han, Minseok Choi, Jaekyun Moon
Alias-Free Generative Adversarial Networks Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, Timo Aila
Noise2Score: Tweedie’s Approach to Self-Supervised Image Denoising without Clean Images Kwanyoung Kim, Jong Chul Ye
Continuous Mean-Covariance Bandits Yihan Du, Siwei Wang, Zhixuan Fang, Longbo Huang
Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language Mingyu Ding, Zhenfang Chen, Tao Du, Ping Luo, Josh Tenenbaum, Chuang Gan
Solving Soft Clustering Ensemble via $k$-Sparse Discrete Wasserstein Barycenter Ruizhe Qin, Mengying Li, Hu Ding
Bayesian Adaptation for Covariate Shift Aurick Zhou, Sergey Levine
Perturb-and-max-product: Sampling and learning in discrete energy-based models Miguel Lazaro-Gredilla, Antoine Dedieu, Dileep George
Towards Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games Xiangyu Liu, Hangtian Jia, Ying Wen, Yujing Hu, Yingfeng Chen, Changjie Fan, ZHIPENG HU, Yaodong Yang
Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples Sungyoon Lee, Woojin Lee, Jinseong Park, Jaewook Lee
Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage Jonathan Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun
Global Filter Networks for Image Classification Yongming Rao, Wenliang Zhao, Zheng Zhu, Jiwen Lu, Jie Zhou
CAFE: Catastrophic Data Leakage in Vertical Federated Learning Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, Tianyi Chen
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan, Bryan Kian Hsiang Low
Compacter: Efficient Low-Rank Hypercomplex Adapter Layers Rabeeh Karimi Mahabadi, James Henderson, Sebastian Ruder
Distilling Image Classifiers in Object Detectors Shuxuan Guo, Jose M. Alvarez, Mathieu Salzmann
Subgroup Generalization and Fairness of Graph Neural Networks Jiaqi Ma, Junwei Deng, Qiaozhu Mei
Scaling Neural Tangent Kernels via Sketching and Random Features Amir Zandieh, Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin
BatchQuant: Quantized-for-all Architecture Search with Robust Quantizer Haoping Bai, Meng Cao, Ping Huang, Jiulong Shan
Long Short-Term Transformer for Online Action Detection Mingze Xu, Yuanjun Xiong, Hao Chen, Xinyu Li, Wei Xia, Zhuowen Tu, Stefano Soatto
Near Optimal Policy Optimization via REPS Aldo Pacchiano, Jonathan N Lee, Peter L. Bartlett, Ofir Nachum
Self-Consistent Models and Values Greg Farquhar, Kate Baumli, Zita Marinho, Angelos Filos, Matteo Hessel, Hado P van Hasselt, David Silver
Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters Takanori Maehara, Hoang NT
Risk-Averse Bayes-Adaptive Reinforcement Learning Marc Rigter, Bruno Lacerda, Nick Hawes
Iterative Connecting Probability Estimation for Networks Yichen Qin, Linhan Yu, Yang Li
Learning to Adapt via Latent Domains for Adaptive Semantic Segmentation Yunan Liu, Shanshan Zhang, Yang Li, Jian Yang
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection Koby Bibas, Meir Feder, Tal Hassner
Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation Lei Ke, Xia Li, Martin Danelljan, Yu-Wing Tai, Chi-Keung Tang, Fisher Yu
Algorithmic Instabilities of Accelerated Gradient Descent Amit Attia, Tomer Koren
Learning Optimal Predictive Checklists Haoran Zhang, Quaid D. Morris, Berk Ustun, Marzyeh Ghassemi
Finite Sample Analysis of Average-Reward TD Learning and $Q$-Learning Sheng Zhang, Zhe Zhang, Siva Theja Maguluri
Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic Atsushi Suzuki, Atsushi Nitanda, jing wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza
Gradient Starvation: A Learning Proclivity in Neural Networks Mohammad Pezeshki, Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, Guillaume Lajoie
Offline Reinforcement Learning as One Big Sequence Modeling Problem Michael Janner, Qiyang Li, Sergey Levine
Optimality and Stability in Federated Learning: A Game-theoretic Approach Kate Donahue, Jon M. Kleinberg
Understanding Deflation Process in Over-parametrized Tensor Decomposition Rong Ge, Yunwei Ren, Xiang Wang, Mo Zhou
Privately Learning Subspaces Vikrant Singhal, Thomas Steinke
On the Value of Interaction and Function Approximation in Imitation Learning Nived Rajaraman, Yanjun Han, Lin Yang, Jingbo Liu, Jiantao Jiao, Kannan Ramchandran
Shapeshifter: a Parameter-efficient Transformer using Factorized Reshaped Matrices Aliakbar Panahi, Seyran Saeedi, Tom Arodz
The Adaptive Doubly Robust Estimator and a Paradox Concerning Logging Policy Masahiro Kato, Kenichiro McAlinn, Shota Yasui
Regularized Softmax Deep Multi-Agent Q-Learning Ling Pan, Tabish Rashid, Bei Peng, Longbo Huang, Shimon Whiteson
Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling Niv Giladi, Zvika Ben-Haim, Sella Nevo, Yossi Matias, Daniel Soudry
Systematic Generalization with Edge Transformers Leon Bergen, Timothy O'Donnell, Dzmitry Bahdanau
TransformerFusion: Monocular RGB Scene Reconstruction using Transformers Aljaz Bozic, Pablo Palafox, Justus Thies, Angela Dai, Matthias Niessner
Maximum Likelihood Training of Score-Based Diffusion Models Yang Song, Conor Durkan, Iain Murray, Stefano Ermon
Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization Tian Ye, Simon S Du
Adaptive Data Augmentation on Temporal Graphs Yiwei Wang, Yujun Cai, Yuxuan Liang, Henghui Ding, Changhu Wang, Siddharth Bhatia, Bryan Hooi
Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond Đ.Khuê Lê-Huu, Karteek Alahari
Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs Taebum Kim, Eunji Jeong, Geon-Woo Kim, Yunmo Koo, Sehoon Kim, Gyeongin Yu, Byung-Gon Chun
Uniform Sampling over Episode Difficulty Sébastien Arnold, Guneet Dhillon, Avinash Ravichandran, Stefano Soatto
Scalable Intervention Target Estimation in Linear Models Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer
Play to Grade: Testing Coding Games as Classifying Markov Decision Process Allen Nie, Emma Brunskill, Chris Piech
Distributional Reinforcement Learning for Multi-Dimensional Reward Functions Pushi Zhang, Xiaoyu Chen, Li Zhao, Wei Xiong, Tao Qin, Tie-Yan Liu
Differentiable Unsupervised Feature Selection based on a Gated Laplacian Ofir Lindenbaum, Uri Shaham, Erez Peterfreund, Jonathan Svirsky, Nicolas Casey, Yuval Kluger
Smooth Bilevel Programming for Sparse Regularization Clarice Poon, Gabriel Peyré
Grounding Representation Similarity Through Statistical Testing Frances Ding, Jean-Stanislas Denain, Jacob Steinhardt
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning Mingde Zhao, Zhen Liu, Sitao Luan, Shuyuan Zhang, Doina Precup, Yoshua Bengio
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation Weitong ZHANG, Dongruo Zhou, Quanquan Gu
Beltrami Flow and Neural Diffusion on Graphs Benjamin Chamberlain, James Rowbottom, Davide Eynard, Francesco Di Giovanni, Xiaowen Dong, Michael Bronstein
Think Big, Teach Small: Do Language Models Distil Occam’s Razor? Gonzalo Jaimovitch-Lopez, David Castellano Falcón, Cesar Ferri, José Hernández-Orallo
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA Hermanni Hälvä, Sylvain Le Corff, Luc Lehéricy, Jonathan So, Yongjie Zhu, Elisabeth Gassiat, Aapo Hyvarinen
Conditionally Parameterized, Discretization-Aware Neural Networks for Mesh-Based Modeling of Physical Systems Jiayang Xu, Aniruddhe Pradhan, Karthikeyan Duraisamy
USCO-Solver: Solving Undetermined Stochastic Combinatorial Optimization Problems Guangmo Tong
Adaptive Conformal Inference Under Distribution Shift Isaac Gibbs, Emmanuel Candes
Periodic Activation Functions Induce Stationarity Lassi Meronen, Martin Trapp, Arno Solin
Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation David Acuna, Jonah Philion, Sanja Fidler
KS-GNN: Keywords Search over Incomplete Graphs via Graphs Neural Network YU HAO, Xin Cao, Yufan Sheng, Yixiang Fang, Wei Wang
Reconstruction for Powerful Graph Representations Leonardo Cotta, Christopher Morris, Bruno Ribeiro
Revealing and Protecting Labels in Distributed Training Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays
Solving Graph-based Public Goods Games with Tree Search and Imitation Learning Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence Qi Qi, Youzhi Luo, Zhao Xu, Shuiwang Ji, Tianbao Yang
Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization Qi Zhu, Carl Yang, Yidan Xu, Haonan Wang, Chao Zhang, Jiawei Han
You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership Xuxi Chen, Tianlong Chen, Zhenyu Zhang, Zhangyang Wang
Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization Haochuan Li, Yi Tian, Jingzhao Zhang, Ali Jadbabaie
Early-stopped neural networks are consistent Ziwei Ji, Justin Li, Matus J. Telgarsky
NxMTransformer: Semi-Structured Sparsification for Natural Language Understanding via ADMM Connor Holmes, Minjia Zhang, Yuxiong He, Bo Wu
Reliable Decisions with Threshold Calibration Roshni Sahoo, Shengjia Zhao, Alyssa Chen, Stefano Ermon
End-to-End Weak Supervision Salva Rühling Cachay, Benedikt Boecking, Artur Dubrawski
Shift Invariance Can Reduce Adversarial Robustness Vasu Singla, Songwei Ge, Basri Ronen, David Jacobs
Wisdom of the Crowd Voting: Truthful Aggregation of Voter Information and Preferences Grant Schoenebeck, Biaoshuai Tao
Replay-Guided Adversarial Environment Design Minqi Jiang, Michael Dennis, Jack Parker-Holder, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning Nathan Grinsztajn, Johan Ferret, Olivier Pietquin, philippe preux, Matthieu Geist
Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics Ingmar Schubert, Danny Driess, Ozgur S. Oguz, Marc Toussaint
Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks Jinhee Lee, Haeri Kim, Youngkyu Hong, Hye Won Chung
Online Multi-Armed Bandits with Adaptive Inference Maria Dimakopoulou, Zhimei Ren, Zhengyuan Zhou
Efficient Truncated Linear Regression with Unknown Noise Variance Constantinos Daskalakis, Patroklos Stefanou, Rui Yao, Emmanouil Zampetakis
Breaking the Dilemma of Medical Image-to-image Translation Lingke Kong, Chenyu Lian, Detian Huang, zhenjiang li, Yanle Hu, Qichao Zhou
Temporally Abstract Partial Models Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, Doina Precup
TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification Shengcai Liao, Ling Shao
Multi-Objective SPIBB: Seldonian Offline Policy Improvement with Safety Constraints in Finite MDPs harsh satija, Philip S. Thomas, Joelle Pineau, Romain Laroche
Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence Alexander Hoyle, Pranav Goel, Andrew Hian-Cheong, Denis Peskov, Jordan Boyd-Graber, Philip Resnik
INDIGO: GNN-Based Inductive Knowledge Graph Completion Using Pair-Wise Encoding Shuwen Liu, Bernardo Grau, Ian Horrocks, Egor Kostylev
Do Input Gradients Highlight Discriminative Features? Harshay Shah, Prateek Jain, Praneeth Netrapalli
Improving Conditional Coverage via Orthogonal Quantile Regression Shai Feldman, Stephen Bates, Yaniv Romano
Minimizing Polarization and Disagreement in Social Networks via Link Recommendation Liwang Zhu, Qi Bao, Zhongzhi Zhang
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations Shasha Li, Abhishek Aich, Shitong Zhu, Salman Asif, Chengyu Song, Amit Roy-Chowdhury, Srikanth Krishnamurthy
Optimal Rates for Random Order Online Optimization Uri Sherman, Tomer Koren, Yishay Mansour
Discrete-Valued Neural Communication Dianbo Liu, Alex M Lamb, Kenji Kawaguchi, Anirudh Goyal ALIAS PARTH GOYAL, Chen Sun, Michael Mozer, Yoshua Bengio
Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method Yifan Chen, Qi Zeng, Heng Ji, Yun Yang
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification Zhuchen Shao, Hao Bian, Yang Chen, Yifeng Wang, Jian Zhang, Xiangyang Ji, yongbing zhang
Multi-view Contrastive Graph Clustering ErLin Pan, Zhao Kang
Inverse-Weighted Survival Games Xintian Han, Mark Goldstein, Aahlad Puli, Thomas Wies, Adler J. Perotte, Rajesh Ranganath
Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform Stability Alec Farid, Anirudha Majumdar
Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement Samuel Daulton, Maximilian Balandat, Eytan Bakshy
Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots Jagdeep Bhatia, Holly Jackson, Yunsheng Tian, Jie Xu, Wojciech Matusik
On Calibration and Out-of-Domain Generalization Yoav Wald, Amir Feder, Daniel Greenfeld, Uri Shalit
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method Junyu Zhang, Chengzhuo Ni, zheng Yu, Csaba Szepesvari, Mengdi Wang
Circa: Stochastic ReLUs for Private Deep Learning Zahra Ghodsi, Nandan Kumar Jha, Brandon Reagen, Siddharth Garg
Reinforcement Learning in Reward-Mixing MDPs Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor
A Gang of Adversarial Bandits Mark Herbster, Stephen Pasteris, Fabio Vitale, Massimiliano Pontil
Explaining Hyperparameter Optimization via Partial Dependence Plots Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl
Robustifying Algorithms of Learning Latent Trees with Vector Variables Fengzhuo Zhang, Vincent Tan
Representation Learning on Spatial Networks Zheng Zhang, Liang Zhao
Continuous-time edge modelling using non-parametric point processes Xuhui Fan, Bin Li, Feng Zhou, Scott SIsson
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time Feng Zhu, Andrew Sedler, Harrison A Grier, Nauman Ahad, Mark Davenport, Matthew Kaufman, Andrea Giovannucci, Chethan Pandarinath
Memory-efficient Patch-based Inference for Tiny Deep Learning Ji Lin, Wei-Ming Chen, Han Cai, Chuang Gan, Song Han
Self-Interpretable Model with Transformation Equivariant Interpretation Yipei Wang, Xiaoqian Wang
Solving Min-Max Optimization with Hidden Structure via Gradient Descent Ascent Emmanouil-Vasileios Vlatakis-Gkaragkounis, Lampros Flokas, Georgios Piliouras
Preserved central model for faster bidirectional compression in distributed settings Constantin Philippenko, Aymeric Dieuleveut
Understanding Instance-based Interpretability of Variational Auto-Encoders Zhifeng Kong, Kamalika Chaudhuri
Voxel-based 3D Detection and Reconstruction of Multiple Objects from a Single Image Feng Liu, Xiaoming Liu
Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization Yusuke Iwasawa, Yutaka Matsuo
Luna: Linear Unified Nested Attention Xuezhe Ma, Xiang Kong, Sinong Wang, Chunting Zhou, Jonathan May, Hao Ma, Luke Zettlemoyer
Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias Raanan Y. Rohekar, Shami Nisimov, Yaniv Gurwicz, Gal Novik
Hindsight Task Relabelling: Experience Replay for Sparse Reward Meta-RL Charles Packer, Pieter Abbeel, Joseph E Gonzalez
A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics Kai Xu, Akash Srivastava, Dan Gutfreund, Felix Sosa, Tomer Ullman, Josh Tenenbaum, Charles A. Sutton
Associating Objects with Transformers for Video Object Segmentation Zongxin Yang, Yunchao Wei, Yi Yang
Automatic Symmetry Discovery with Lie Algebra Convolutional Network Nima Dehmamy, Robin Walters, Yanchen Liu, Dashun Wang, Rose Yu
Zero Time Waste: Recycling Predictions in Early Exit Neural Networks Maciej Wołczyk, Bartosz Wójcik, Klaudia Bałazy, Igor T Podolak, Jacek Tabor, Marek Śmieja, Tomasz Trzcinski
On Model Calibration for Long-Tailed Object Detection and Instance Segmentation Tai-Yu Pan, Cheng Zhang, Yandong Li, Hexiang Hu, Dong Xuan, Soravit Changpinyo, Boqing Gong, Wei-Lun Chao
ReSSL: Relational Self-Supervised Learning with Weak Augmentation Mingkai Zheng, Shan You, Fei Wang, Chen Qian, Changshui Zhang, Xiaogang Wang, Chang Xu
Learning to See by Looking at Noise Manel Baradad Jurjo, Jonas Wulff, Tongzhou Wang, Phillip Isola, Antonio Torralba
Explicit loss asymptotics in the gradient descent training of neural networks Maksim Velikanov, Dmitry Yarotsky
Test-Time Personalization with a Transformer for Human Pose Estimation Yizhuo Li, Miao Hao, Zonglin Di, Nitesh Bharadwaj Gundavarapu, Xiaolong Wang
Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN Zhenyu Xie, Zaiyu Huang, Fuwei Zhao, Haoye Dong, Michael Kampffmeyer, Xiaodan Liang
Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models Hannah Rose Kirk, Yennie Jun, Filippo Volpin, Haider Iqbal, Elias Benussi, Frederic Dreyer, Aleksandar Shtedritski, Yuki Asano
Weisfeiler and Lehman Go Cellular: CW Networks Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yuguang Wang, Pietro Liò, Guido F. Montufar, Michael Bronstein
Learning Conjoint Attentions for Graph Neural Nets Tiantian He, Yew Soon Ong, L Bai
Hybrid Regret Bounds for Combinatorial Semi-Bandits and Adversarial Linear Bandits Shinji Ito
Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling Hongyu Gong, Yun Tang, Juan Pino, Xian Li
Cardinality-Regularized Hawkes-Granger Model Tsuyoshi Ide, Georgios Kollias, Dzung Phan, Naoki Abe
Aligned Structured Sparsity Learning for Efficient Image Super-Resolution Yulun Zhang, Huan Wang, Can Qin, Yun Fu
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong
Constrained Robust Submodular Partitioning Shengjie Wang, Tianyi Zhou, Chandrashekhar Lavania, Jeff A Bilmes
Online Knapsack with Frequency Predictions Sungjin Im, Ravi Kumar, Mahshid Montazer Qaem, Manish Purohit
On Component Interactions in Two-Stage Recommender Systems Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus
Lip to Speech Synthesis with Visual Context Attentional GAN Minsu Kim, Joanna Hong, Yong Man Ro
Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis Jikai Jin, Bohang Zhang, Haiyang Wang, Liwei Wang
Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning Kibeom Kim, Min Whoo Lee, Yoonsung Kim, JeHwan Ryu, Minsu Lee, Byoung-Tak Zhang
Smooth Normalizing Flows Jonas Köhler, Andreas Krämer, Frank Noe
MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images Shaofei Wang, Marko Mihajlovic, Qianli Ma, Andreas Geiger, Siyu Tang
Distributed Principal Component Analysis with Limited Communication Foivos Alimisis, Peter Davies, Bart Vandereycken, Dan Alistarh
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update Michal Derezinski, Jonathan Lacotte, Mert Pilanci, Michael W. Mahoney
Confident Anchor-Induced Multi-Source Free Domain Adaptation Jiahua Dong, Zhen Fang, Anjin Liu, Gan Sun, Tongliang Liu
Word2Fun: Modelling Words as Functions for Diachronic Word Representation Benyou Wang, Emanuele Di Buccio, Massimo Melucci
Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate Christian Kümmerle, Claudio Mayrink Verdun, Dominik Stöger
Low-Rank Constraints for Fast Inference in Structured Models Justin Chiu, Yuntian Deng, Alexander Rush
Accumulative Poisoning Attacks on Real-time Data Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, Jun Zhu
UCB-based Algorithms for Multinomial Logistic Regression Bandits Sanae Amani, Christos Thrampoulidis
Estimating the Long-Term Effects of Novel Treatments Keith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Miruna Oprescu, Vasilis Syrgkanis
Dual Progressive Prototype Network for Generalized Zero-Shot Learning Chaoqun Wang, Shaobo Min, Xuejin Chen, Xiaoyan Sun, Houqiang Li
Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity Kaiqing Zhang, Xiangyuan Zhang, Bin Hu, Tamer Basar
G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators Yunhui Long, Boxin Wang, Zhuolin Yang, Bhavya Kailkhura, Aston Zhang, Carl Gunter, Bo Li
On the Existence of The Adversarial Bayes Classifier Pranjal Awasthi, Natalie Frank, Mehryar Mohri
Convex-Concave Min-Max Stackelberg Games Denizalp Goktas, Amy Greenwald
Misspecified Gaussian Process Bandit Optimization Ilija Bogunovic, Andreas Krause
Visual Adversarial Imitation Learning using Variational Models Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn
Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning Jongjin Park, Younggyo Seo, Chang Liu, Li Zhao, Tao Qin, Jinwoo Shin, Tie-Yan Liu
Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detection Chunjong Park, Anas Awadalla, Tadayoshi Kohno, Shwetak Patel
Multiclass Boosting and the Cost of Weak Learning Nataly Brukhim, Elad Hazan, Shay Moran, Indraneel Mukherjee, Robert E. Schapire
Partition-Based Formulations for Mixed-Integer Optimization of Trained ReLU Neural Networks Calvin Tsay, Jan Kronqvist, Alexander Thebelt, Ruth Misener
Hyperparameter Optimization Is Deceiving Us, and How to Stop It A. Feder Cooper, Yucheng Lu, Jessica Forde, Christopher M De Sa
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning Alireza Fallah, Kristian Georgiev, Aryan Mokhtari, Asuman Ozdaglar
3D Pose Transfer with Correspondence Learning and Mesh Refinement Chaoyue Song, Jiacheng Wei, Ruibo Li, Fayao Liu, Guosheng Lin
Framing RNN as a kernel method: A neural ODE approach Adeline Fermanian, Pierre Marion, Jean-Philippe Vert, Gérard Biau
Contextual Similarity Aggregation with Self-attention for Visual Re-ranking Jianbo Ouyang, Hui Wu, Min Wang, Wengang Zhou, Houqiang Li
Can Information Flows Suggest Targets for Interventions in Neural Circuits? Praveen Venkatesh, Sanghamitra Dutta, Neil Mehta, Pulkit Grover
AutoBalance: Optimized Loss Functions for Imbalanced Data Mingchen Li, Xuechen Zhang, Christos Thrampoulidis, Jiasi Chen, Samet Oymak
SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes Zhaozhi Qian, Yao Zhang, Ioana Bica, Angela Wood, Mihaela van der Schaar
Statistical Query Lower Bounds for List-Decodable Linear Regression Ilias Diakonikolas, Daniel Kane, Ankit Pensia, Thanasis Pittas, Alistair Stewart
Unsupervised Motion Representation Learning with Capsule Autoencoders Ziwei Xu, Xudong Shen, Yongkang Wong, Mohan S Kankanhalli
VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media Yizhou Zhang, Karishma Sharma, Yan Liu
An Improved Analysis and Rates for Variance Reduction under Without-replacement Sampling Orders Xinmeng Huang, Kun Yuan, Xianghui Mao, Wotao Yin
Exploring Forensic Dental Identification with Deep Learning Yuan Liang, Weikun Han, Liang Qiu, Chen Wu, Yiting Shao, Kun Wang, Lei He
Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training Yuanhao Cai, Xiaowan Hu, Haoqian Wang, Yulun Zhang, Hanspeter Pfister, Donglai Wei
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks Jianhong Wang, Wangkun Xu, Yunjie Gu, Wenbin Song, Tim C Green
Looking Beyond Single Images for Contrastive Semantic Segmentation Learning FEIHU ZHANG, Philip Torr, Rene Ranftl, Stephan Richter
A Constant Approximation Algorithm for Sequential Random-Order No-Substitution k-Median Clustering Tom Hess, Michal Moshkovitz, Sivan Sabato
Dangers of Bayesian Model Averaging under Covariate Shift Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, Andrew G Wilson
Learning Equilibria in Matching Markets from Bandit Feedback Meena Jagadeesan, Alexander Wei, Yixin Wang, Michael I. Jordan, Jacob Steinhardt
Towards Lower Bounds on the Depth of ReLU Neural Networks Christoph Hertrich, Amitabh Basu, Marco Di Summa, Martin Skutella
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective Geoff Pleiss, John P. Cunningham
Exact marginal prior distributions of finite Bayesian neural networks Jacob Zavatone-Veth, Cengiz Pehlevan
Spatiotemporal Joint Filter Decomposition in 3D Convolutional Neural Networks Zichen Miao, Ze Wang, Xiuyuan Cheng, Qiang Qiu
Pooling by Sliced-Wasserstein Embedding Navid Naderializadeh, Joseph F Comer, Reed Andrews, Heiko Hoffmann, Soheil Kolouri
On the Theory of Reinforcement Learning with Once-per-Episode Feedback Niladri Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees Kuan-Lin Chen, Ching-Hua Lee, Harinath Garudadri, Bhaskar D Rao
Locally private online change point detection Tom Berrett, Yi Yu
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish
Repulsive Deep Ensembles are Bayesian Francesco D'Angelo, Vincent Fortuin
BayesIMP: Uncertainty Quantification for Causal Data Fusion Siu Lun Chau, Jean-Francois Ton, Javier González, Yee W. Teh, Dino Sejdinovic
RMM: Reinforced Memory Management for Class-Incremental Learning Yaoyao Liu, Bernt Schiele, Qianru Sun
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM) Jie Bu, Arka Daw, M. Maruf, Anuj Karpatne
Neural Auto-Curricula in Two-Player Zero-Sum Games Xidong Feng, Oliver Slumbers, Ziyu Wan, Bo Liu, Stephen McAleer, Ying Wen, Jun Wang, Yaodong Yang
ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis Patrick Esser, Robin Rombach, Andreas Blattmann, Bjorn Ommer
From global to local MDI variable importances for random forests and when they are Shapley values Antonio Sutera, Gilles Louppe, Van Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts
Adversarial Robustness of Streaming Algorithms through Importance Sampling Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou
Tractable Regularization of Probabilistic Circuits Anji Liu, Guy Van den Broeck
On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness Eric Mintun, Alexander Kirillov, Saining Xie
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data Ashraful Islam, Chun-Fu (Richard) Chen, Rameswar Panda, Leonid Karlinsky, Rogerio Feris, Richard J. Radke
Hypergraph Propagation and Community Selection for Objects Retrieval Guoyuan An, Yuchi Huo, Sung-eui Yoon
Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space Taiji Suzuki, Atsushi Nitanda
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning Kaan Ozkara, Navjot Singh, Deepesh Data, Suhas Diggavi
Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu
The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations Peter Hase, Harry Xie, Mohit Bansal
Control Variates for Slate Off-Policy Evaluation Nikos Vlassis, Ashok Chandrashekar, Fernando Amat, Nathan Kallus
Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation Nicklas Hansen, Hao Su, Xiaolong Wang
On Effective Scheduling of Model-based Reinforcement Learning Hang Lai, Jian Shen, Weinan Zhang, Yimin Huang, Xing Zhang, Ruiming Tang, Yong Yu, Zhenguo Li
Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience Dominic Gonschorek, Larissa Höfling, Klaudia P. Szatko, Katrin Franke, Timm Schubert, Benjamin Dunn, Philipp Berens, David Klindt, Thomas Euler
Learning Knowledge Graph-based World Models of Textual Environments Prithviraj Ammanabrolu, Mark Riedl
Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization Ke Sun, Yafei Wang, Yi Liu, yingnan zhao, Bo Pan, Shangling Jui, Bei Jiang, Linglong Kong
Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components Nate Veldt, Austin R Benson, Jon M. Kleinberg
Episodic Multi-agent Reinforcement Learning with Curiosity-driven Exploration Lulu Zheng, Jiarui Chen, Jianhao Wang, Jiamin He, Yujing Hu, Yingfeng Chen, Changjie Fan, Yang Gao, Chongjie Zhang
Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution Amrith Setlur, Oscar Li, Virginia Smith
Debiased Visual Question Answering from Feature and Sample Perspectives Zhiquan Wen, Guanghui Xu, Mingkui Tan, Qingyao Wu, Qi Wu
Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, Quanshi Zhang
On the Out-of-distribution Generalization of Probabilistic Image Modelling Mingtian Zhang, Andi Zhang, Steven McDonagh
Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach Qiujiang Jin, Aryan Mokhtari
PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations Moshe Eliasof, Eldad Haber, Eran Treister
Information Directed Reward Learning for Reinforcement Learning David Lindner, Matteo Turchetta, Sebastian Tschiatschek, Kamil Ciosek, Andreas Krause
SSMF: Shifting Seasonal Matrix Factorization Koki Kawabata, Siddharth Bhatia, Rui Liu, Mohit Wadhwa, Bryan Hooi
Associative Memories via Predictive Coding Tommaso Salvatori, Yuhang Song, Yujian Hong, Lei Sha, Simon Frieder, Zhenghua Xu, Rafal Bogacz, Thomas Lukasiewicz
Robust and differentially private mean estimation Xiyang Liu, Weihao Kong, Sham Kakade, Sewoong Oh
Adaptable Agent Populations via a Generative Model of Policies Kenneth Derek, Phillip Isola
A No-go Theorem for Robust Acceleration in the Hyperbolic Plane Linus Hamilton, Ankur Moitra
Privately Learning Mixtures of Axis-Aligned Gaussians Ishaq Aden-Ali, Hassan Ashtiani, Christopher Liaw
Deep Self-Dissimilarities as Powerful Visual Fingerprints Idan Kligvasser, Tamar Shaham, Yuval Bahat, Tomer Michaeli
Invariant Causal Imitation Learning for Generalizable Policies Ioana Bica, Daniel Jarrett, Mihaela van der Schaar
CoAtNet: Marrying Convolution and Attention for All Data Sizes Zihang Dai, Hanxiao Liu, Quoc V Le, Mingxing Tan
Mixed Supervised Object Detection by Transferring Mask Prior and Semantic Similarity Yan Liu, Zhijie Zhang, Li Niu, Junjie Chen, Liqing Zhang
Celebrating Diversity in Shared Multi-Agent Reinforcement Learning Chenghao Li, Tonghan Wang, Chengjie Wu, Qianchuan Zhao, Jun Yang, Chongjie Zhang
Rebounding Bandits for Modeling Satiation Effects Liu Leqi, Fatma Kilinc Karzan, Zachary Lipton, Alan Montgomery
Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond Maria-Florina F Balcan, Siddharth Prasad, Tuomas Sandholm, Ellen Vitercik
IQ-Learn: Inverse soft-Q Learning for Imitation Divyansh Garg, Shuvam Chakraborty, Chris Cundy, Jiaming Song, Stefano Ermon
Task-Agnostic Undesirable Feature Deactivation Using Out-of-Distribution Data Dongmin Park, Hwanjun Song, Minseok Kim, Jae-Gil Lee
Private Non-smooth ERM and SCO in Subquadratic Steps Janardhan Kulkarni, Yin Tat Lee, Daogao Liu
Towards Instance-Optimal Offline Reinforcement Learning with Pessimism Ming Yin, Yu-Xiang Wang
Speedy Performance Estimation for Neural Architecture Search Robin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk, Yarin Gal
How Tight Can PAC-Bayes be in the Small Data Regime? Andrew Foong, Wessel Bruinsma, David Burt, Richard Turner
Deep Synoptic Monte-Carlo Planning in Reconnaissance Blind Chess Gregory Clark
Dynamic Analysis of Higher-Order Coordination in Neuronal Assemblies via De-Sparsified Orthogonal Matching Pursuit Shoutik Mukherjee, Behtash Babadi
Efficient Training of Retrieval Models using Negative Cache Erik Lindgren, Sashank Reddi, Ruiqi Guo, Sanjiv Kumar
Understanding Partial Multi-Label Learning via Mutual Information Xiuwen Gong, Dong Yuan, Wei Bao
Environment Generation for Zero-Shot Compositional Reinforcement Learning Izzeddin Gur, Natasha Jaques, Yingjie Miao, Jongwook Choi, Manoj Tiwari, Honglak Lee, Aleksandra Faust
Optimizing Conditional Value-At-Risk of Black-Box Functions Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
E(n) Equivariant Normalizing Flows Victor Garcia Satorras, Emiel Hoogeboom, Fabian Fuchs, Ingmar Posner, Max Welling
Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning Chongjian GE, Youwei Liang, YIBING SONG, Jianbo Jiao, Jue Wang, Ping Luo
A Critical Look at the Consistency of Causal Estimation with Deep Latent Variable Models Severi Rissanen, Pekka Marttinen
Improving Robustness using Generated Data Sven Gowal, Sylvestre-Alvise Rebuffi, Olivia Wiles, Florian Stimberg, Dan Andrei Calian, Timothy A Mann
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A Erdogdu
Learning to Learn Graph Topologies Xingyue Pu, Tianyue Cao, Xiaoyun Zhang, Xiaowen Dong, Siheng Chen
Invertible Tabular GANs: Killing Two Birds with One Stone for Tabular Data Synthesis JAEHOON LEE, Jihyeon Hyeong, Jinsung Jeon, Noseong Park, Jihoon Cho
Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks Chenning Yu, Sicun Gao
Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons Wenbo Ren, Jia Liu, Ness Shroff
Efficient Bayesian network structure learning via local Markov boundary search Ming Gao, Bryon Aragam
Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention Byung-Hoon Kim, Jong Chul Ye, Jae-Jin Kim
Understanding the Generalization Benefit of Model Invariance from a Data Perspective Sicheng Zhu, Bang An, Furong Huang
Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP Zihan Zhang, Jiaqi Yang, Xiangyang Ji, Simon S Du
How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness? Xinshuai Dong, Anh Tuan Luu, Min Lin, Shuicheng Yan, Hanwang Zhang
Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networks Robert Lieck, Martin Rohrmeier
EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback Peter Richtarik, Igor Sokolov, Ilyas Fatkhullin
Mixture weights optimisation for Alpha-Divergence Variational Inference Kamélia Daudel, randal douc
Instance-dependent Label-noise Learning under a Structural Causal Model Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang
Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration Gavin Kerrigan, Padhraic Smyth, Mark Steyvers
$\texttt{LeadCache}$: Regret-Optimal Caching in Networks Debjit Paria, Abhishek Sinha
Probabilistic Attention for Interactive Segmentation Prasad Gabbur, Manjot Bilkhu, Javier R. Movellan
Influence Patterns for Explaining Information Flow in BERT Kaiji Lu, Zifan Wang, Piotr Mardziel, Anupam Datta
Robust Regression Revisited: Acceleration and Improved Estimation Rates Arun Jambulapati, Jerry Li, Tselil Schramm, Kevin Tian
Automatic Unsupervised Outlier Model Selection Yue Zhao, Ryan Rossi, Leman Akoglu
Pruning Randomly Initialized Neural Networks with Iterative Randomization Daiki Chijiwa, Shin'ya Yamaguchi, Yasutoshi Ida, Kenji Umakoshi, Tomohiro INOUE
Probing Inter-modality: Visual Parsing with Self-Attention for Vision-and-Language Pre-training Hongwei Xue, Yupan Huang, Bei Liu, Houwen Peng, Jianlong Fu, Houqiang Li, Jiebo Luo
Stability and Generalization of Bilevel Programming in Hyperparameter Optimization Fan Bao, Guoqiang Wu, Chongxuan LI, Jun Zhu, Bo Zhang
Regime Switching Bandits Xiang Zhou, Yi Xiong, Ningyuan Chen, Xuefeng GAO
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps Awais Muhammad, Fengwei Zhou, Chuanlong Xie, Jiawei Li, Sung-Ho Bae, Zhenguo Li
Localization, Convexity, and Star Aggregation Suhas Vijaykumar
Aligning Silhouette Topology for Self-Adaptive 3D Human Pose Recovery Ramesha Rakesh Mugaludi, Jogendra Nath Kundu, Varun Jampani, Venkatesh Babu R
Self-Adaptable Point Processes with Nonparametric Time Decays Zhimeng Pan, Zheng Wang, Jeff M Phillips, Shandian Zhe
Offline Meta Reinforcement Learning -- Identifiability Challenges and Effective Data Collection Strategies Ron Dorfman, Idan Shenfeld, Aviv Tamar
RoMA: Robust Model Adaptation for Offline Model-based Optimization Sihyun Yu, Sungsoo Ahn, Le Song, Jinwoo Shin
Flexible Option Learning Martin Klissarov, Doina Precup
Faster Directional Convergence of Linear Neural Networks under Spherically Symmetric Data Dachao Lin, Ruoyu Sun, Zhihua Zhang
Online Facility Location with Multiple Advice Matteo Almanza, Flavio Chierichetti, Silvio Lattanzi, Alessandro Panconesi, Giuseppe Re
Credit Assignment in Neural Networks through Deep Feedback Control Alexander Meulemans, Matilde Tristany Farinha, Javier Garcia Ordonez, Pau Vilimelis Aceituno, João Sacramento, Benjamin F. Grewe
Robust Online Correlation Clustering Silvio Lattanzi, Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang, Rudy Zhou
Neural Additive Models: Interpretable Machine Learning with Neural Nets Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Ben Lengerich, Rich Caruana, Geoffrey E. Hinton
Representation Learning for Event-based Visuomotor Policies Sai Vemprala, Sami Mian, Ashish Kapoor
Kernel Functional Optimisation Arun Kumar Anjanapura Venkatesh, Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh
Generalized Shape Metrics on Neural Representations Alex H Williams, Erin Kunz, Simon Kornblith, Scott Linderman
Diverse Message Passing for Attribute with Heterophily Liang Yang, Mengzhe Li, Liyang Liu, bingxin niu, Chuan Wang, Xiaochun Cao, Yuanfang Guo
Towards Robust Bisimulation Metric Learning Mete Kemertas, Tristan Aumentado-Armstrong
Beyond BatchNorm: Towards a Unified Understanding of Normalization in Deep Learning Ekdeep S Lubana, Robert Dick, Hidenori Tanaka
Representation Learning Beyond Linear Prediction Functions Ziping Xu, Ambuj Tewari
Volume Rendering of Neural Implicit Surfaces Lior Yariv, Jiatao Gu, Yoni Kasten, Yaron Lipman
MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, Zaid Harchaoui
Accurately Solving Rod Dynamics with Graph Learning Han Shao, Tassilo Kugelstadt, Torsten Hädrich, Wojtek Palubicki, Jan Bender, Soeren Pirk, Dominik L Michels
Limiting fluctuation and trajectorial stability of multilayer neural networks with mean field training Huy Tuan Pham, Phan-Minh Nguyen
Medical Dead-ends and Learning to Identify High-Risk States and Treatments Mehdi Fatemi, Taylor W Killian, Jayakumar Subramanian, Marzyeh Ghassemi
Overcoming the Convex Barrier for Simplex Inputs Harkirat Singh Behl, M. Pawan Kumar, Philip Torr, Krishnamurthy Dvijotham
High-probability Bounds for Non-Convex Stochastic Optimization with Heavy Tails Ashok Cutkosky, Harsh Mehta
Batch Normalization Orthogonalizes Representations in Deep Random Networks Hadi Daneshmand, Amir Joudaki, Francis R. Bach
Support vector machines and linear regression coincide with very high-dimensional features Navid Ardeshir, Clayton Sanford, Daniel J. Hsu
Coupled Segmentation and Edge Learning via Dynamic Graph Propagation Zhiding Yu, Rui Huang, Wonmin Byeon, Sifei Liu, Guilin Liu, Thomas Breuel, Anima Anandkumar, Jan Kautz
Offline RL Without Off-Policy Evaluation David Brandfonbrener, Will Whitney, Rajesh Ranganath, Joan Bruna
Continuous vs. Discrete Optimization of Deep Neural Networks Omer Elkabetz, Nadav Cohen
CrypTen: Secure Multi-Party Computation Meets Machine Learning Brian Knott, Shobha Venkataraman, Awni Hannun, Shubho Sengupta, Mark Ibrahim, Laurens van der Maaten
Can contrastive learning avoid shortcut solutions? Joshua W. Robinson, Li Sun, Ke Yu, Kayhan Batmanghelich, Stefanie Jegelka, Suvrit Sra
See More for Scene: Pairwise Consistency Learning for Scene Classification Gongwei Chen, Xinhang Song, Bohan Wang, Shuqiang Jiang
Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss Jeff Z. HaoChen, Colin Wei, Adrien Gaidon, Tengyu Ma
Greedy Approximation Algorithms for Active Sequential Hypothesis Testing Kyra Gan, Su Jia, Andrew Li
Score-based Generative Neural Networks for Large-Scale Optimal Transport Mara Daniels, Tyler Maunu, Paul Hand
When False Positive is Intolerant: End-to-End Optimization with Low FPR for Multipartite Ranking Peisong Wen, Qianqian Xu, Zhiyong Yang, Yuan He, Qingming Huang
Convex Polytope Trees Mohammadreza Armandpour, Ali Sadeghian, Mingyuan Zhou
The Skellam Mechanism for Differentially Private Federated Learning Naman Agarwal, Peter Kairouz, Ziyu Liu
Stability and Deviation Optimal Risk Bounds with Convergence Rate $O(1/n)$ Yegor Klochkov, Nikita Zhivotovskiy
SketchGen: Generating Constrained CAD Sketches Wamiq Para, Shariq Bhat, Paul Guerrero, Tom Kelly, Niloy Mitra, Leonidas Guibas, Peter Wonka
CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation Ankit Singh
Differentially Private n-gram Extraction Kunho Kim, Sivakanth Gopi, Janardhan Kulkarni, Sergey Yekhanin
Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic representations Joy Hsu, Jeffrey Gu, Gong Wu, Wah Chiu, Serena Yeung
Noisy Recurrent Neural Networks Soon Hoe Lim, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney
Matrix encoding networks for neural combinatorial optimization Yeong-Dae Kwon, Jinho Choo, Iljoo Yoon, Minah Park, Duwon Park, Youngjune Gwon
When Is Unsupervised Disentanglement Possible? Daniella Horan, Eitan Richardson, Yair Weiss
Continuous Latent Process Flows Ruizhi Deng, Marcus A Brubaker, Greg Mori, Andreas Lehrmann
Perturbation-based Regret Analysis of Predictive Control in Linear Time Varying Systems Yiheng Lin, Yang Hu, Guanya Shi, Haoyuan Sun, Guannan Qu, Adam Wierman
Dataset Distillation with Infinitely Wide Convolutional Networks Timothy Nguyen, Roman Novak, Lechao Xiao, Jaehoon Lee
SPANN: Highly-efficient Billion-scale Approximate Nearest Neighborhood Search Qi Chen, Bing Zhao, Haidong Wang, Mingqin Li, Chuanjie Liu, Zengzhong Li, Mao Yang, Jingdong Wang
Distilling Object Detectors with Feature Richness Du Zhixing, Rui Zhang, Ming Chang, xishan zhang, Shaoli Liu, Tianshi Chen, Yunji Chen
Analysis of one-hidden-layer neural networks via the resolvent method Vanessa Piccolo, Dominik Schröder
Grounding Spatio-Temporal Language with Transformers Tristan Karch, Laetitia Teodorescu, Katja Hofmann, Clément Moulin-Frier, Pierre-Yves Oudeyer
Learning where to learn: Gradient sparsity in meta and continual learning Johannes von Oswald, Dominic Zhao, Seijin Kobayashi, Simon Schug, Massimo Caccia, Nicolas Zucchet, João Sacramento
Domain Invariant Representation Learning with Domain Density Transformations A. Tuan Nguyen, Toan Tran, Yarin Gal, Atilim Gunes Baydin
PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning Tao Yu, Cuiling Lan, Wenjun Zeng, Mingxiao Feng, Zhizheng Zhang, Zhibo Chen
Efficient Equivariant Network Lingshen He, Yuxuan Chen, zhengyang shen, Yiming Dong, Yisen Wang, Zhouchen Lin
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation Yunhao Tang, Tadashi Kozuno, Mark Rowland, Remi Munos, Michal Valko
Even your Teacher Needs Guidance: Ground-Truth Targets Dampen Regularization Imposed by Self-Distillation Kenneth Borup, Lars N Andersen
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition Lucas Liebenwein, Alaa Maalouf, Dan Feldman, Daniela Rus
Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann Machines Aurélien Decelle, Cyril Furtlehner, Beatriz Seoane
Imitation with Neural Density Models Kuno Kim, Akshat Jindal, Yang Song, Jiaming Song, Yanan Sui, Stefano Ermon
Accurate Point Cloud Registration with Robust Optimal Transport Zhengyang Shen, Jean Feydy, Peirong Liu, Ariel H Curiale, Ruben San Jose Estepar, Raul San Jose Estepar, Marc Niethammer
Simple steps are all you need: Frank-Wolfe and generalized self-concordant functions Alejandro Carderera, Mathieu Besançon, Sebastian Pokutta
Automatic Data Augmentation for Generalization in Reinforcement Learning Roberta Raileanu, Maxwell Goldstein, Denis Yarats, Ilya Kostrikov, Rob Fergus
Blending Anti-Aliasing into Vision Transformer Shengju Qian, Hao Shao, Yi Zhu, Mu Li, Jiaya Jia
A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration Theo Bodrito, Alexandre Zouaoui, Jocelyn Chanussot, Julien Mairal
Posterior Collapse and Latent Variable Non-identifiability Yixin Wang, David M. Blei, John P. Cunningham
The Benefits of Implicit Regularization from SGD in Least Squares Problems Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham Kakade
Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks Alireza Fallah, Aryan Mokhtari, Asuman Ozdaglar
Factored Policy Gradients: Leveraging Structure for Efficient Learning in MOMDPs Thomas Spooner, Nelson Vadori, Sumitra Ganesh
MarioNette: Self-Supervised Sprite Learning Dmitriy Smirnov, MICHAEL GHARBI, Matthew Fisher, Vitor Guizilini, Alexei Efros, Justin M Solomon
RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem Eric Liang, Zhanghao Wu, Michael Luo, Sven Mika, Joseph E Gonzalez, Ion Stoica
Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correction Gal Dalal, Assaf Hallak, Steven Dalton, iuri frosio, Shie Mannor, Gal Chechik
Redesigning the Transformer Architecture with Insights from Multi-particle Dynamical Systems Subhabrata Dutta, Tanya Gautam, Soumen Chakrabarti, Tanmoy Chakraborty
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Center Smoothing: Certified Robustness for Networks with Structured Outputs Aounon Kumar, Tom Goldstein
Breaking the Linear Iteration Cost Barrier for Some Well-known Conditional Gradient Methods Using MaxIP Data-structures Zhaozhuo Xu, Zhao Song, Anshumali Shrivastava
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A Topological Perspective on Causal Inference Duligur Ibeling, Thomas Icard
Parameter Inference with Bifurcation Diagrams Gregory Szep, Neil Dalchau, Attila Csikász-Nagy
Scalable Thompson Sampling using Sparse Gaussian Process Models Sattar Vakili, Henry Moss, Artem Artemev, Vincent Dutordoir, Victor Picheny
Robust Counterfactual Explanations on Graph Neural Networks Mohit Bajaj, Lingyang Chu, Zi Yu Xue, Jian Pei, Lanjun Wang, Peter Cho-Ho Lam, Yong Zhang
Similarity and Matching of Neural Network Representations Adrián Csiszárik, Péter Kőrösi-Szabó, Ákos Matszangosz, Gergely Papp, Dániel Varga
DOCTOR: A Simple Method for Detecting Misclassification Errors Federica Granese, Marco Romanelli, Daniele Gorla, Catuscia Palamidessi, Pablo Piantanida
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On the Variance of the Fisher Information for Deep Learning Alexander Soen, Ke Sun
A$^2$-Net: Learning Attribute-Aware Hash Codes for Large-Scale Fine-Grained Image Retrieval Xiu-Shen Wei, Yang Shen, Xuhao Sun, Han-Jia Ye, Jian Yang
Shape Registration in the Time of Transformers Giovanni Trappolini, Luca Cosmo, Luca Moschella, Riccardo Marin, Simone Melzi, Emanuele Rodolà
Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning Hyunsoo Chung, Jungtaek Kim, Boris Knyazev, Jinhwi Lee, Graham W. Taylor, Jaesik Park, Minsu Cho
Dissecting the Diffusion Process in Linear Graph Convolutional Networks Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin
Dynamic Grained Encoder for Vision Transformers Lin Song, Songyang Zhang, Songtao Liu, Zeming Li, Xuming He, Hongbin Sun, Jian Sun, Nanning Zheng
Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning Kento Nozawa, Issei Sato
On UMAP's True Loss Function Sebastian Damrich, Fred A. Hamprecht
Fast Pure Exploration via Frank-Wolfe Po-An Wang, Ruo-Chun Tzeng, Alexandre Proutiere
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History Aware Multimodal Transformer for Vision-and-Language Navigation Shizhe Chen, Pierre-Louis Guhur, Cordelia Schmid, Ivan Laptev
Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data Feng Liu, Wenkai Xu, Jie Lu, Danica J. Sutherland
Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets Irene Solaiman, Christy Dennison
The Lazy Online Subgradient Algorithm is Universal on Strongly Convex Domains Daron Anderson, Douglas Leith
Computer-Aided Design as Language Yaroslav Ganin, Sergey Bartunov, Yujia Li, Ethan Keller, Stefano Saliceti
COHESIV: Contrastive Object and Hand Embedding Segmentation In Video Dandan Shan, Richard Higgins, David Fouhey
ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE Qingzhong Ai, LIRONG HE, SHIYU LIU, Zenglin Xu
Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition Jiaming Liu, Salman Asif, Brendt Wohlberg, Ulugbek Kamilov
Group Equivariant Subsampling Jin Xu, Hyunjik Kim, Thomas Rainforth, Yee W. Teh
Data Sharing and Compression for Cooperative Networked Control Jiangnan Cheng, Marco Pavone, Sachin Katti, Sandeep Chinchali, Ao Tang
Hyperbolic Procrustes Analysis Using Riemannian Geometry Ya-Wei Eileen Lin, Yuval Kluger, Ronen Talmon
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data Mi Luo, Fei Chen, Dapeng Hu, Yifan Zhang, Jian Liang, Jiashi Feng
Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization Jialun Zhang, Salar Fattahi, Richard Y Zhang
Improving Contrastive Learning on Imbalanced Data via Open-World Sampling Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang
Searching for Efficient Transformers for Language Modeling David So, Wojciech Mańke, Hanxiao Liu, Zihang Dai, Noam Shazeer, Quoc V Le
Scaling Ensemble Distribution Distillation to Many Classes with Proxy Targets Max Ryabinin, Andrey Malinin, Mark Gales
Multi-Person 3D Motion Prediction with Multi-Range Transformers Jiashun Wang, Huazhe Xu, Medhini Narasimhan, Xiaolong Wang
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning Prashant Khanduri, PRANAY SHARMA, Haibo Yang, Mingyi Hong, Jia Liu, Ketan Rajawat, Pramod Varshney
Bubblewrap: Online tiling and real-time flow prediction on neural manifolds Anne Draelos, Pranjal Gupta, Na Young Jun, Chaichontat Sriworarat, John C. Pearson
The Semi-Random Satisfaction of Voting Axioms Lirong Xia
Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis Tianchang Shen, Jun Gao, Kangxue Yin, Ming-Yu Liu, Sanja Fidler
Learning to Combine Per-Example Solutions for Neural Program Synthesis Disha Shrivastava, Hugo Larochelle, Daniel Tarlow
On Success and Simplicity: A Second Look at Transferable Targeted Attacks Zhengyu Zhao, Zhuoran Liu, Martha Larson
Provably efficient, succinct, and precise explanations Guy Blanc, Jane Lange, Li-Yang Tan
Refined Learning Bounds for Kernel and Approximate $k$-Means Yong Liu
Learning Causal Semantic Representation for Out-of-Distribution Prediction Chang Liu, Xinwei Sun, Jindong Wang, Haoyue Tang, Tao Li, Tao Qin, Wei Chen, Tie-Yan Liu
A first-order primal-dual method with adaptivity to local smoothness Maria-Luiza Vladarean, Yura Malitsky, Volkan Cevher
A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning Pan Zhou, Caiming Xiong, Xiaotong Yuan, Steven Chu Hong Hoi
Adversarial Robustness with Semi-Infinite Constrained Learning Alexander Robey, Luiz Chamon, George J. Pappas, Hamed Hassani, Alejandro Ribeiro
Conformal Time-series Forecasting Kamile Stankeviciute, Ahmed M. Alaa, Mihaela van der Schaar
A 3D Generative Model for Structure-Based Drug Design Shitong Luo, Jiaqi Guan, Jianzhu Ma, Jian Peng
Bootstrapping the Error of Oja's Algorithm Robert Lunde, Purnamrita Sarkar, Rachel Ward
Landscape analysis of an improved power method for tensor decomposition Joe Kileel, Timo Klock, João M Pereira
Curriculum Offline Imitating Learning Minghuan Liu, Hanye Zhao, Zhengyu Yang, Jian Shen, Weinan Zhang, Li Zhao, Tie-Yan Liu
Robust Pose Estimation in Crowded Scenes with Direct Pose-Level Inference Dongkai Wang, Shiliang Zhang, Gang Hua
Ising Model Selection Using $\ell_{1}$-Regularized Linear Regression: A Statistical Mechanics Analysis Xiangming Meng, Tomoyuki Obuchi, Yoshiyuki Kabashima
Conformal Prediction using Conditional Histograms Matteo Sesia, Yaniv Romano
Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels Sheng Wan, Yibing Zhan, Liu Liu, Baosheng Yu, Shirui Pan, Chen Gong
Collaborative Uncertainty in Multi-Agent Trajectory Forecasting Bohan Tang, Yiqi Zhong, Ulrich Neumann, Gang Wang, Siheng Chen, Ya Zhang
Network-to-Network Regularization: Enforcing Occam's Razor to Improve Generalization Rohan Ghosh, Mehul Motani
Generalized and Discriminative Few-Shot Object Detection via SVD-Dictionary Enhancement Aming WU, Suqi Zhao, Cheng Deng, Wei Liu
Conditioning Sparse Variational Gaussian Processes for Online Decision-making Wesley J Maddox, Samuel Stanton, Andrew G Wilson
Spherical Motion Dynamics: Learning Dynamics of Normalized Neural Network using SGD and Weight Decay Ruosi Wan, Zhanxing Zhu, Xiangyu Zhang, Jian Sun
Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations Jiayao Zhang, Hua Wang, Weijie Su
Probabilistic Forecasting: A Level-Set Approach Hilaf Hasson, Bernie Wang, Tim Januschowski, Jan Gasthaus
Roto-translated Local Coordinate Frames For Interacting Dynamical Systems Miltiadis Kofinas, Naveen Nagaraja, Efstratios Gavves
ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions Luigi Carratino, Stefano Vigogna, Daniele Calandriello, Lorenzo Rosasco
Scaling Gaussian Processes with Derivative Information Using Variational Inference Misha Padidar, Xinran Zhu, Leo Huang, Jacob Gardner, David Bindel
On the Representation of Solutions to Elliptic PDEs in Barron Spaces Ziang Chen, Jianfeng Lu, Yulong Lu
A/B Testing for Recommender Systems in a Two-sided Marketplace Preetam Nandy, Divya Venugopalan, Chun Lo, Shaunak Chatterjee
Retiring Adult: New Datasets for Fair Machine Learning Frances Ding, Moritz Hardt, John P. Miller, Ludwig Schmidt
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Self-Instantiated Recurrent Units with Dynamic Soft Recursion Aston Zhang, Yi Tay, Yikang Shen, Alvin Chan, SHUAI ZHANG
Sparse Uncertainty Representation in Deep Learning with Inducing Weights Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li
Scalable Inference of Sparsely-changing Gaussian Markov Random Fields Salar Fattahi, Andres Gomez
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation Jixuan Wang, Kuan-Chieh Wang, Frank Rudzicz, Michael Brudno
Learnability of Linear Thresholds from Label Proportions Rishi Saket
A variational approximate posterior for the deep Wishart process Sebastian Ober, Laurence Aitchison
Neural Pseudo-Label Optimism for the Bank Loan Problem Aldo Pacchiano, Shaun Singh, Edward Chou, Alex Berg, Jakob Foerster
Visualizing the Emergence of Intermediate Visual Patterns in DNNs Mingjie Li, Shaobo Wang, Quanshi Zhang
Learning 3D Dense Correspondence via Canonical Point Autoencoder An-Chieh Cheng, Xueting Li, Min Sun, Ming-Hsuan Yang, Sifei Liu
Speech-T: Transducer for Text to Speech and Beyond Jiawei Chen, Xu Tan, Yichong Leng, Jin Xu, Guihua Wen, Tao Qin, Tie-Yan Liu
Multi-modal Dependency Tree for Video Captioning Wentian Zhao, Xinxiao Wu, Jiebo Luo
Greedy and Random Quasi-Newton Methods with Faster Explicit Superlinear Convergence Dachao Lin, Haishan Ye, Zhihua Zhang
Neural Tangent Kernel Maximum Mean Discrepancy Xiuyuan Cheng, Yao Xie
Subgraph Federated Learning with Missing Neighbor Generation Ke ZHANG, Carl Yang, Xiaoxiao Li, Lichao Sun, Siu Ming Yiu
Bellman-consistent Pessimism for Offline Reinforcement Learning Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein
Sub-Linear Memory: How to Make Performers SLiM Valerii Likhosherstov, Krzysztof M Choromanski, Jared Quincy Davis, Xingyou Song, Adrian Weller
Efficient Learning of Discrete-Continuous Computation Graphs David Friede, Mathias Niepert
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson, Furong Huang, Tom Goldstein
Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima Guangyuan SHI, JIAXIN CHEN, Wenlong Zhang, Li-Ming Zhan, Xiao-Ming Wu
Functional Neural Networks for Parametric Image Restoration Problems Fangzhou Luo, Xiaolin Wu, Yanhui Guo
Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks Tolga Birdal, Aaron Lou, Leonidas Guibas, Umut Simsekli
GemNet: Universal Directional Graph Neural Networks for Molecules Johannes Gasteiger, Florian Becker, Stephan Günnemann
Loss function based second-order Jensen inequality and its application to particle variational inference Futoshi Futami, Tomoharu Iwata, naonori ueda, Issei Sato, Masashi Sugiyama
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning Aodong Li, Alex Boyd, Padhraic Smyth, Stephan Mandt
Asynchronous Decentralized SGD with Quantized and Local Updates Giorgi Nadiradze, Amirmojtaba Sabour, Peter Davies, Shigang Li, Dan Alistarh
Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret Jean Tarbouriech, Runlong Zhou, Simon S Du, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Nested Counterfactual Identification from Arbitrary Surrogate Experiments Juan Correa, Sanghack Lee, Elias Bareinboim
Sim and Real: Better Together Shirli Di-Castro, Dotan Di Castro, Shie Mannor
Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions Huan Ma, Zongbo Han, Changqing Zhang, Huazhu Fu, Joey Tianyi Zhou, Qinghua Hu
An Empirical Study of Adder Neural Networks for Object Detection Xinghao Chen, Chang Xu, Minjing Dong, Chunjing XU, Yunhe Wang
Does Knowledge Distillation Really Work? Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A Alemi, Andrew G Wilson
Teachable Reinforcement Learning via Advice Distillation Olivia Watkins, Abhishek Gupta, Trevor Darrell, Pieter Abbeel, Jacob Andreas
Antipodes of Label Differential Privacy: PATE and ALIBI Mani Malek Esmaeili, Ilya Mironov, Karthik Prasad, Igor Shilov, Florian Tramer
Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases Shashi Kant Gupta, Mengmi Zhang, CHIA-CHIEN WU, Jeremy Wolfe, Gabriel Kreiman
On the Universality of Graph Neural Networks on Large Random Graphs Nicolas Keriven, Alberto Bietti, Samuel Vaiter
Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees Gregory Dexter, Kevin Bello, Jean Honorio
Adversarial Attacks on Graph Classifiers via Bayesian Optimisation Xingchen Wan, Henry Kenlay, Robin Ru, Arno Blaas, Michael A Osborne, Xiaowen Dong
Regulating algorithmic filtering on social media Sarah Cen, Devavrat Shah
argmax centroid Chengyue Gong, Mao Ye, Qiang Liu
Contrastive Learning of Global and Local Video Representations shuang ma, Zhaoyang Zeng, Daniel McDuff, Yale Song
BooVI: Provably Efficient Bootstrapped Value Iteration Boyi Liu, Qi Cai, Zhuoran Yang, Zhaoran Wang
Do Wider Neural Networks Really Help Adversarial Robustness? Boxi Wu, Jinghui Chen, Deng Cai, Xiaofei He, Quanquan Gu
Exploring the Limits of Out-of-Distribution Detection Stanislav Fort, Jie Ren, Balaji Lakshminarayanan
ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning Hyuck Lee, Seungjae Shin, Heeyoung Kim
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery Chris Cundy, Aditya Grover, Stefano Ermon
Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks Iulia Duta, Andrei Nicolicioiu, Marius Leordeanu
Information-constrained optimization: can adaptive processing of gradients help? Jayadev Acharya, Clement Canonne, Prathamesh Mayekar, Himanshu Tyagi
Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective Zhengzhuo Xu, Zenghao Chai, Chun Yuan
Learning to Draw: Emergent Communication through Sketching Daniela Mihai, Jonathon Hare
Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks Jesse Hagenaars, Federico Paredes-Valles, Guido de Croon
On the Value of Infinite Gradients in Variational Autoencoder Models Bin Dai, Li Wenliang, David P. Wipf
Online Robust Reinforcement Learning with Model Uncertainty Yue Wang, Shaofeng Zou
Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose Angtian Wang, Shenxiao Mei, Alan L. Yuille, Adam Kortylewski
Sharp Impossibility Results for Hyper-graph Testing Jiashun Jin, Zheng Tracy Ke, Jiajun Liang
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning Yangsibo Huang, Samyak Gupta, Zhao Song, Kai Li, Sanjeev Arora
Faster Non-asymptotic Convergence for Double Q-learning Lin Zhao, Huaqing Xiong, Yingbin Liang
Towards Tight Communication Lower Bounds for Distributed Optimisation Janne H. Korhonen, Dan Alistarh
Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification Jiong Zhang, Wei-Cheng Chang, Hsiang-Fu Yu, Inderjit S. Dhillon
HRFormer: High-Resolution Vision Transformer for Dense Predict YUHUI YUAN, Rao Fu, Lang Huang, Weihong Lin, Chao Zhang, Xilin Chen, Jingdong Wang
Manifold Topology Divergence: a Framework for Comparing Data Manifolds. Serguei Barannikov, Ilya Trofimov, Grigorii Sotnikov, Ekaterina Trimbach, Alexander Korotin, Alexander Filippov, Evgeny Burnaev
Weak-shot Fine-grained Classification via Similarity Transfer Junjie Chen, Li Niu, Liu Liu, Liqing Zhang
Shape your Space: A Gaussian Mixture Regularization Approach to Deterministic Autoencoders Amrutha Saseendran, Kathrin Skubch, Stefan Falkner, Margret Keuper
An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning Blake E Woodworth, Nathan Srebro
Indexed Minimum Empirical Divergence for Unimodal Bandits Hassan SABER, Pierre Ménard, Odalric-Ambrym Maillard
SOAT: A Scene- and Object-Aware Transformer for Vision-and-Language Navigation Abhinav Moudgil, Arjun Majumdar, Harsh Agrawal, Stefan Lee, Dhruv Batra
A Normative and Biologically Plausible Algorithm for Independent Component Analysis Yanis Bahroun, Dmitri B. Chklovskii, Anirvan Sengupta
Regret Bounds for Gaussian-Process Optimization in Large Domains Manuel Wuethrich, Bernhard Schölkopf, Andreas Krause
Deeply Shared Filter Bases for Parameter-Efficient Convolutional Neural Networks Woochul Kang, Daeyeon Kim
On Optimal Robustness to Adversarial Corruption in Online Decision Problems Shinji Ito
Directed Spectrum Measures Improve Latent Network Models Of Neural Populations Neil Gallagher, Kafui Dzirasa, David E. Carlson
Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble Gaon An, Seungyong Moon, Jang-Hyun Kim, Hyun Oh Song
Distribution-free inference for regression: discrete, continuous, and in between Yonghoon Lee, Rina Barber
Statistical Inference with M-Estimators on Adaptively Collected Data Kelly Zhang, Lucas Janson, Susan Murphy
NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang
LSH-SMILE: Locality Sensitive Hashing Accelerated Simulation and Learning Chonghao Sima, Yexiang Xue
Meta-learning with an Adaptive Task Scheduler Huaxiu Yao, Yu Wang, Ying Wei, Peilin Zhao, Mehrdad Mahdavi, Defu Lian, Chelsea Finn
Neural Active Learning with Performance Guarantees Zhilei Wang, Pranjal Awasthi, Christoph Dann, Ayush Sekhari, Claudio Gentile
A Gradient Method for Multilevel Optimization Ryo Sato, Mirai Tanaka, Akiko Takeda
Edge Representation Learning with Hypergraphs Jaehyeong Jo, Jinheon Baek, Seul Lee, Dongki Kim, Minki Kang, Sung Ju Hwang
One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval Akari Asai, Xinyan Yu, Jungo Kasai, Hanna Hajishirzi
LEADS: Learning Dynamical Systems that Generalize Across Environments Yuan Yin, Ibrahim Ayed, Emmanuel de Bézenac, Nicolas Baskiotis, Patrick Gallinari
Storchastic: A Framework for General Stochastic Automatic Differentiation Emile Krieken, Jakub Tomczak, Annette Ten Teije
Concentration inequalities under sub-Gaussian and sub-exponential conditions Andreas Maurer, Massimiliano Pontil
Variance-Aware Off-Policy Evaluation with Linear Function Approximation Yifei Min, Tianhao Wang, Dongruo Zhou, Quanquan Gu
A Provably Efficient Sample Collection Strategy for Reinforcement Learning Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Improved Regret Bounds for Tracking Experts with Memory James Robinson, Mark Herbster
Robustness of Graph Neural Networks at Scale Simon Geisler, Tobias Schmidt, Hakan Şirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann
Random Noise Defense Against Query-Based Black-Box Attacks Zeyu Qin, Yanbo Fan, Hongyuan Zha, Baoyuan Wu
SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL Ruichu Cai, Jinjie Yuan, Boyan Xu, Zhifeng Hao
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction Ming Yin, Yu Bai, Yu-Xiang Wang
Joint Modeling of Visual Objects and Relations for Scene Graph Generation Minghao Xu, Meng Qu, Bingbing Ni, Jian Tang
Going Beyond Linear Transformers with Recurrent Fast Weight Programmers Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber
Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimization Bing-Jing Hsieh, Ping-Chun Hsieh, Xi Liu
Forster Decomposition and Learning Halfspaces with Noise Ilias Diakonikolas, Daniel Kane, Christos Tzamos
Cortico-cerebellar networks as decoupling neural interfaces Joseph Pemberton, Ellen Boven, Richard Apps, Rui Ponte Costa
To The Point: Correspondence-driven monocular 3D category reconstruction Filippos Kokkinos, Iasonas Kokkinos
Proper Value Equivalence Christopher Grimm, Andre Barreto, Greg Farquhar, David Silver, Satinder P. Singh
Challenges and Opportunities in High Dimensional Variational Inference Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael R Andersen, Jonathan Huggins, Aki Vehtari
On the Expressivity of Markov Reward David Abel, Will Dabney, Anna Harutyunyan, Mark K Ho, Michael L. Littman, Doina Precup, Satinder P. Singh
One More Step Towards Reality: Cooperative Bandits with Imperfect Communication Udari Madhushani, Abhimanyu Dubey, Naomi Leonard, Alex Pentland
Multi-Agent Reinforcement Learning in Stochastic Networked Systems Yiheng Lin, Guannan Qu, Longbo Huang, Adam Wierman
Neural Scene Flow Prior Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey
The future is log-Gaussian: ResNets and their infinite-depth-and-width limit at initialization Mufan Li, Mihai Nica, Dan Roy
Grammar-Based Grounded Lexicon Learning Jiayuan Mao, Freda Shi, Jiajun Wu, Roger P. Levy, Josh Tenenbaum
Distributed Deep Learning In Open Collaborations Michael Diskin, Alexey Bukhtiyarov, Max Ryabinin, Lucile Saulnier, quentin lhoest, Anton Sinitsin, Dmitry Popov, Dmitry V. Pyrkin, Maxim Kashirin, Alexander Borzunov, Albert Villanova del Moral, Denis Mazur, Ilia Kobelev, Yacine Jernite, Thomas Wolf, Gennady Pekhimenko
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris C Holmes, Frank Hutter, Yee W. Teh
Finding Bipartite Components in Hypergraphs Peter Macgregor, He Sun
Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation Soojung Yang, Doyeong Hwang, Seul Lee, Seongok Ryu, Sung Ju Hwang
Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent Spencer Frei, Quanquan Gu
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation Gavin Brown, Marco Gaboardi, Adam Smith, Jonathan Ullman, Lydia Zakynthinou
Label consistency in overfitted generalized $k$-means Linfan Zhang, Arash Amini
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise Hongxin Wei, Lue Tao, RENCHUNZI XIE, Bo An
The Complexity of Sparse Tensor PCA Davin Choo, Tommaso d'Orsi
Learning to Elect Cem Anil, Xuchan Bao
KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support Pierre Glaser, Michael Arbel, Arthur Gretton
When Is Generalizable Reinforcement Learning Tractable? Dhruv Malik, Yuanzhi Li, Pradeep K. Ravikumar
Relational Self-Attention: What's Missing in Attention for Video Understanding Manjin Kim, Heeseung Kwon, CHUNYU WANG, Suha Kwak, Minsu Cho
Towards Enabling Meta-Learning from Target Models Su Lu, Han-Jia Ye, Le Gan, De-Chuan Zhan
A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models Ibrahim M Alabdulmohsin, Mario Lucic
GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement Martin Engelcke, Oiwi Parker Jones, Ingmar Posner
How Data Augmentation affects Optimization for Linear Regression Boris Hanin, Yi Sun
An Exact Characterization of the Generalization Error for the Gibbs Algorithm Gholamali Aminian, Yuheng Bu, Laura Toni, Miguel Rodrigues, Gregory Wornell
Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and Learning Alberto Maria Metelli, Alessio Russo, Marcello Restelli
Rethinking gradient sparsification as total error minimization Atal Sahu, Aritra Dutta, Ahmed M. Abdelmoniem, Trambak Banerjee, Marco Canini, Panos Kalnis
Approximate optimization of convex functions with outlier noise Anindya De, Sanjeev Khanna, Huan Li, MohammadHesam NikpeySalekde
Fair Classification with Adversarial Perturbations L. Elisa Celis, Anay Mehrotra, Nisheeth Vishnoi
Distributed Saddle-Point Problems Under Data Similarity Aleksandr Beznosikov, Gesualdo Scutari, Alexander Rogozin, Alexander Gasnikov
Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces Aryan Deshwal, Jana Doppa
Gradual Domain Adaptation without Indexed Intermediate Domains Hong-You Chen, Wei-Lun Chao
K-level Reasoning for Zero-Shot Coordination in Hanabi Brandon Cui, Hengyuan Hu, Luis Pineda, Jakob Foerster
Learning Markov State Abstractions for Deep Reinforcement Learning Cameron Allen, Neev Parikh, Omer Gottesman, George Konidaris
Towards Deeper Deep Reinforcement Learning with Spectral Normalization Nils Bjorck, Carla P. Gomes, Kilian Q. Weinberger
Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery Huaxiu Yao, Ying Wei, Long-Kai Huang, Ding Xue, Junzhou Huang, Zhenhui (Jessie) Li
Memory-Efficient Approximation Algorithms for Max-k-Cut and Correlation Clustering Nimita Shinde, Vishnu Narayanan, James Saunderson
Panoptic 3D Scene Reconstruction From a Single RGB Image Manuel Dahnert, Ji Hou, Matthias Niessner, Angela Dai
Measuring Generalization with Optimal Transport Ching-Yao Chuang, Youssef Mroueh, Kristjan Greenewald, Antonio Torralba, Stefanie Jegelka
Uniform Concentration Bounds toward a Unified Framework for Robust Clustering Debolina Paul, Saptarshi Chakraborty, Swagatam Das, Jason Xu
Learning Signal-Agnostic Manifolds of Neural Fields Yilun Du, Katie Collins, Josh Tenenbaum, Vincent Sitzmann
Low-dimensional Structure in the Space of Language Representations is Reflected in Brain Responses Richard Antonello, Javier S Turek, Vy Vo, Alexander Huth
On the Suboptimality of Thompson Sampling in High Dimensions Raymond Zhang, Richard Combes
Learning Debiased and Disentangled Representations for Semantic Segmentation Sanghyeok Chu, Dongwan Kim, Bohyung Han
Diversity Matters When Learning From Ensembles Giung Nam, Jongmin Yoon, Yoonho Lee, Juho Lee
Locally Valid and Discriminative Prediction Intervals for Deep Learning Models Zhen Lin, Shubhendu Trivedi, Jimeng Sun
Personalized Federated Learning With Gaussian Processes Idan Achituve, Aviv Shamsian, Aviv Navon, Gal Chechik, Ethan Fetaya
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures Yuan Cao, Quanquan Gu, Mikhail Belkin
Implicit SVD for Graph Representation Learning Sami Abu-El-Haija, Hesham Mostafa, Marcel Nassar, Valentino Crespi, Greg Ver Steeg, Aram Galstyan
Offline Model-based Adaptable Policy Learning Xiong-Hui Chen, Yang Yu, Qingyang Li, Fan-Ming Luo, Zhiwei Qin, Wenjie Shang, Jieping Ye
Multilingual Pre-training with Universal Dependency Learning Kailai Sun, Zuchao Li, Hai Zhao
Parameter-free HE-friendly Logistic Regression Junyoung Byun, Woojin Lee, Jaewook Lee
Active clustering for labeling training data Quentin Lutz, Elie de Panafieu, Maya Stein, Alex Scott
Exploring Social Posterior Collapse in Variational Autoencoder for Interaction Modeling Chen Tang, Wei Zhan, Masayoshi Tomizuka
Ensembling Graph Predictions for AMR Parsing Thanh Lam Hoang, Gabriele Picco, Yufang Hou, Young-Suk Lee, Lam Nguyen, Dzung Phan, Vanessa Lopez, Ramon Fernandez Astudillo
On the interplay between data structure and loss function in classification problems Stéphane d'Ascoli, Marylou Gabrié, Levent Sagun, Giulio Biroli
Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems Suhas Kowshik, Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli
Mixture Proportion Estimation and PU Learning:A Modern Approach Saurabh Garg, Yifan Wu, Alexander J Smola, Sivaraman Balakrishnan, Zachary Lipton
Escape saddle points by a simple gradient-descent based algorithm Chenyi Zhang, Tongyang Li
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks Alexandra Peste, Eugenia Iofinova, Adrian Vladu, Dan Alistarh
HyperSPNs: Compact and Expressive Probabilistic Circuits Andy Shih, Dorsa Sadigh, Stefano Ermon
Scaling Vision with Sparse Mixture of Experts Carlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, Neil Houlsby
Two-sided fairness in rankings via Lorenz dominance Virginie Do, Sam Corbett-Davies, Jamal Atif, Nicolas Usunier
Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel Dominic Richards, Ilja Kuzborskij
Adversarial Intrinsic Motivation for Reinforcement Learning Ishan Durugkar, Mauricio Tec, Scott Niekum, Peter Stone
Machine Learning for Variance Reduction in Online Experiments Yongyi Guo, Dominic Coey, Mikael Konutgan, Wenting Li, Chris Schoener, Matt Goldman
L2ight: Enabling On-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimization Jiaqi Gu, Hanqing Zhu, Chenghao Feng, Zixuan Jiang, Ray Chen, David Pan
Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond Risheng Liu, Yaohua Liu, Shangzhi Zeng, Jin Zhang
Multi-Facet Clustering Variational Autoencoders Fabian Falck, Haoting Zhang, Matthew Willetts, George Nicholson, Christopher Yau, Chris C Holmes
Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls Nick Doudchenko, Khashayar Khosravi, Jean Pouget-Abadie, Sébastien Lahaie, Miles Lubin, Vahab Mirrokni, Jann Spiess, guido imbens
Ranking Policy Decisions Hadrien Pouget, Hana Chockler, Youcheng Sun, Daniel Kroening
Searching the Search Space of Vision Transformer Minghao Chen, Kan Wu, Bolin Ni, Houwen Peng, Bei Liu, Jianlong Fu, Hongyang Chao, Haibin Ling
Relative stability toward diffeomorphisms indicates performance in deep nets Leonardo Petrini, Alessandro Favero, Mario Geiger, Matthieu Wyart
Raw Nav-merge Seismic Data to Subsurface Properties with MLP based Multi-Modal Information Unscrambler Aditya Desai, Zhaozhuo Xu, Menal Gupta, Anu Chandran, Antoine Vial-Aussavy, Anshumali Shrivastava
Inverse Problems Leveraging Pre-trained Contrastive Representations Sriram Ravula, Georgios Smyrnis, Matt Jordan, Alexandros G Dimakis
The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation Thibault Sejourne, Francois-Xavier Vialard, Gabriel Peyré
Diffusion Models Beat GANs on Image Synthesis Prafulla Dhariwal, Alexander Nichol
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning Kai Wang, Sanket Shah, Haipeng Chen, Andrew Perrault, Finale Doshi-Velez, Milind Tambe
A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms Anand Kalvit, Assaf J. Zeevi
SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization Amir Hertz, Or Perel, Raja Giryes, Olga Sorkine-hornung, Daniel Cohen-or
A Biased Graph Neural Network Sampler with Near-Optimal Regret Qingru Zhang, David P. Wipf, Quan Gan, Le Song
Equilibrium Refinement for the Age of Machines: The One-Sided Quasi-Perfect Equilibrium Gabriele Farina, Tuomas Sandholm
Interpreting Representation Quality of DNNs for 3D Point Cloud Processing Wen Shen, Qihan Ren, Dongrui Liu, Quanshi Zhang
How Fine-Tuning Allows for Effective Meta-Learning Kurtland Chua, Qi Lei, Jason Lee
Cooperative Stochastic Bandits with Asynchronous Agents and Constrained Feedback Lin Yang, Yu-Zhen Janice Chen, Stephen Pasteris, Mohammad Hajiesmaili, John C. S. Lui, Don Towsley
Multiple Descent: Design Your Own Generalization Curve Lin Chen, Yifei Min, Mikhail Belkin, Amin Karbasi
On Empirical Risk Minimization with Dependent and Heavy-Tailed Data Abhishek Roy, Krishnakumar Balasubramanian, Murat A Erdogdu
Gone Fishing: Neural Active Learning with Fisher Embeddings Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Sham Kakade
On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry Andi Han, Bamdev Mishra, Pratik Kumar Jawanpuria, Junbin Gao
Refining Language Models with Compositional Explanations Huihan Yao, Ying Chen, Qinyuan Ye, Xisen Jin, Xiang Ren
Going Beyond Linear RL: Sample Efficient Neural Function Approximation Baihe Huang, Kaixuan Huang, Sham Kakade, Jason Lee, Qi Lei, Runzhe Wang, Jiaqi Yang
Scalable Neural Data Server: A Data Recommender for Transfer Learning Tianshi Cao, Sasha (Alexandre) Doubov, David Acuna, Sanja Fidler
What can linearized neural networks actually say about generalization? Guillermo Ortiz-Jimenez, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard
CATs: Cost Aggregation Transformers for Visual Correspondence Seokju Cho, Sunghwan Hong, Sangryul Jeon, Yunsung Lee, Kwanghoon Sohn, Seungryong Kim
Asynchronous Stochastic Optimization Robust to Arbitrary Delays Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain
Consistent Non-Parametric Methods for Maximizing Robustness Robi Bhattacharjee, Kamalika Chaudhuri
Generalizable Multi-linear Attention Network Tao Jin, Zhou Zhao
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning Muhan Zhang, Pan Li, Yinglong Xia, Kai Wang, Long Jin
SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients Feihu Huang, Junyi Li, Heng Huang
General Nonlinearities in SO(2)-Equivariant CNNs Daniel Franzen, Michael Wand
Denoising Normalizing Flow Christian Horvat, Jean-Pascal Pfister
Attention over Learned Object Embeddings Enables Complex Visual Reasoning David Ding, Felix Hill, Adam Santoro, Malcolm Reynolds, Matt Botvinick
Differentially Private Federated Bayesian Optimization with Distributed Exploration Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
Differentiable Learning Under Triage Nastaran Okati, Abir De, Manuel Rodriguez
ROI Maximization in Stochastic Online Decision-Making Nicolò Cesa-Bianchi, Tom Cesari, Yishay Mansour, Vianney Perchet
When Expressivity Meets Trainability: Fewer than $n$ Neurons Can Work Jiawei Zhang, Yushun Zhang, Mingyi Hong, Ruoyu Sun, Zhi-Quan Luo
Analyzing the Confidentiality of Undistillable Teachers in Knowledge Distillation Souvik Kundu, Qirui Sun, Yao Fu, Massoud Pedram, Peter Beerel
High Probability Complexity Bounds for Line Search Based on Stochastic Oracles Billy Jin, Katya Scheinberg, Miaolan Xie
Pay Attention to MLPs Hanxiao Liu, Zihang Dai, David So, Quoc V Le
An Image is Worth More Than a Thousand Words: Towards Disentanglement in The Wild Aviv Gabbay, Niv Cohen, Yedid Hoshen
Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models Courtney Paquette, Elliot Paquette
Adversarial Examples in Multi-Layer Random ReLU Networks Peter L. Bartlett, Sebastien Bubeck, Yeshwanth Cherapanamjeri
Efficient Statistical Assessment of Neural Network Corruption Robustness Karim TIT, Teddy Furon, Mathias ROUSSET
A Highly-Efficient Group Elastic Net Algorithm with an Application to Function-On-Scalar Regression Tobia Boschi, Matthew Reimherr, Francesca Chiaromonte
Hierarchical Clustering: $O(1)$-Approximation for Well-Clustered Graphs Bogdan-Adrian Manghiuc, He Sun
Realistic evaluation of transductive few-shot learning Olivier Veilleux, Malik Boudiaf, Pablo Piantanida, Ismail Ben Ayed
Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes Sanghyun Hong, Michael-Andrei Panaitescu-Liess, Yigitcan Kaya, Tudor Dumitras
Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings Raef Bassily, Cristóbal Guzmán, Michael Menart
TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning Minchao Wu, Michael Norrish, Christian Walder, Amir Dezfouli
Integrating Tree Path in Transformer for Code Representation Han Peng, Ge Li, Wenhan Wang, YunFei Zhao, Zhi Jin
Twins: Revisiting the Design of Spatial Attention in Vision Transformers Xiangxiang Chu, Zhi Tian, Yuqing Wang, Bo Zhang, Haibing Ren, Xiaolin Wei, Huaxia Xia, Chunhua Shen
Evaluating State-of-the-Art Classification Models Against Bayes Optimality Ryan Theisen, Huan Wang, Lav R. Varshney, Caiming Xiong, Richard Socher
Data-Efficient Instance Generation from Instance Discrimination Ceyuan Yang, Yujun Shen, Yinghao Xu, Bolei Zhou
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability Dylan Slack, Anna Hilgard, Sameer Singh, Himabindu Lakkaraju
Learning Graph Models for Retrosynthesis Prediction Vignesh Ram Somnath, Charlotte Bunne, Connor Coley, Andreas Krause, Regina Barzilay
Differentiable Equilibrium Computation with Decision Diagrams for Stackelberg Models of Combinatorial Congestion Games Shinsaku Sakaue, Kengo Nakamura
Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System Matthias Schultheis, Dominik Straub, Constantin A Rothkopf
Deep Neural Networks as Point Estimates for Deep Gaussian Processes Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande
Locality defeats the curse of dimensionality in convolutional teacher-student scenarios Alessandro Favero, Francesco Cagnetta, Matthieu Wyart
Causal Identification with Matrix Equations Sanghack Lee, Elias Bareinboim
Private and Non-private Uniformity Testing for Ranking Data Róbert Busa-Fekete, Dimitris Fotakis, Emmanouil Zampetakis
Model-Based Reinforcement Learning via Imagination with Derived Memory Yao Mu, Yuzheng Zhuang, Bin Wang, Guangxiang Zhu, Wulong Liu, Jianyu Chen, Ping Luo, Shengbo Li, Chongjie Zhang, Jianye Hao
Compositional Transformers for Scene Generation Dor Arad Hudson, Larry Zitnick
An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap Yuanhao Wang, Ruosong Wang, Sham Kakade
Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification Zhenyu Wang, Ya-Li Li, Ye Guo, Shengjin Wang
Reducing the Covariate Shift by Mirror Samples in Cross Domain Alignment Yin Zhao, minquan wang, Longjun Cai
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning Robin Winter, Frank Noe, Djork-Arné Clevert
Causal Abstractions of Neural Networks Atticus Geiger, Hanson Lu, Thomas Icard, Christopher Potts
Conic Blackwell Algorithm: Parameter-Free Convex-Concave Saddle-Point Solving Julien Grand-Clément, Christian Kroer
3DP3: 3D Scene Perception via Probabilistic Programming Nishad Gothoskar, Marco Cusumano-Towner, Ben Zinberg, Matin Ghavamizadeh, Falk Pollok, Austin Garrett, Josh Tenenbaum, Dan Gutfreund, Vikash K. Mansinghka
Novel Upper Bounds for the Constrained Most Probable Explanation Task Tahrima Rahman, Sara Rouhani, Vibhav Gogate
Why Spectral Normalization Stabilizes GANs: Analysis and Improvements Zinan Lin, Vyas Sekar, Giulia Fanti
$(\textrm{Implicit})^2$: Implicit Layers for Implicit Representations Zhichun Huang, Shaojie Bai, J. Zico Kolter
Mean-based Best Arm Identification in Stochastic Bandits under Reward Contamination Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das
MADE: Exploration via Maximizing Deviation from Explored Regions Tianjun Zhang, Paria Rashidinejad, Jiantao Jiao, Yuandong Tian, Joseph E Gonzalez, Stuart J. Russell
Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems Jiayu Chen, Yuanxin Zhang, Yuanfan Xu, Huimin Ma, Huazhong Yang, Jiaming Song, Yu Wang, Yi Wu
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, Steven Chu Hong Hoi
Variational Model Inversion Attacks Kuan-Chieh Wang, YAN FU, Ke Li, Ashish Khisti, Richard S. Zemel, Alireza Makhzani
Graph Neural Networks with Adaptive Residual Xiaorui Liu, Jiayuan Ding, Wei Jin, Han Xu, Yao Ma, Zitao Liu, Jiliang Tang
Efficient Active Learning for Gaussian Process Classification by Error Reduction Guang Zhao, Edward Dougherty, Byung-Jun Yoon, Francis Alexander, Xiaoning Qian
Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation Yue Wang, Shaofeng Zou, Yi Zhou
A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks Jacob Springer, Melanie Mitchell, Garrett Kenyon
TriBERT: Human-centric Audio-visual Representation Learning Tanzila Rahman, Mengyu Yang, Leonid Sigal
How does a Neural Network's Architecture Impact its Robustness to Noisy Labels? Jingling Li, Mozhi Zhang, Keyulu Xu, John Dickerson, Jimmy Ba
Calibration and Consistency of Adversarial Surrogate Losses Pranjal Awasthi, Natalie Frank, Anqi Mao, Mehryar Mohri, Yutao Zhong
The Value of Information When Deciding What to Learn Dilip Arumugam, Benjamin Van Roy
Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning Hiroki Furuta, Tadashi Kozuno, Tatsuya Matsushima, Yutaka Matsuo, Shixiang (Shane) Gu
Can fMRI reveal the representation of syntactic structure in the brain? Aniketh Janardhan Reddy, Leila Wehbe
Robust Implicit Networks via Non-Euclidean Contractions Saber Jafarpour, Alexander Davydov, Anton Proskurnikov, Francesco Bullo
A Kernel-based Test of Independence for Cluster-correlated Data Hongjiao Liu, Anna Plantinga, Yunhua Xiang, Michael Wu
Efficient methods for Gaussian Markov random fields under sparse linear constraints David Bolin, Jonas Wallin
Sparse is Enough in Scaling Transformers Sebastian Jaszczur, Aakanksha Chowdhery, Afroz Mohiuddin, LUKASZ KAISER, Wojciech Gajewski, Henryk Michalewski, Jonni Kanerva
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi, Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu
Low-Fidelity Video Encoder Optimization for Temporal Action Localization Mengmeng Xu, Juan Manuel Perez Rua, Xiatian Zhu, Bernard Ghanem, Brais Martinez
On Provable Benefits of Depth in Training Graph Convolutional Networks Weilin Cong, Morteza Ramezani, Mehrdad Mahdavi
Practical Near Neighbor Search via Group Testing Joshua Engels, Benjamin Coleman, Anshumali Shrivastava
Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others Kanishk Gandhi, Gala Stojnic, Brenden M Lake, Moira R Dillon
Neural Hybrid Automata: Learning Dynamics With Multiple Modes and Stochastic Transitions Michael Poli, Stefano Massaroli, Luca Scimeca, Sanghyuk Chun, Seong Joon Oh, Atsushi Yamashita, Hajime Asama, Jinkyoo Park, Animesh Garg
Fast Projection onto the Capped Simplex with Applications to Sparse Regression in Bioinformatics Man Shun Ang, Jianzhu Ma, Nianjun Liu, Kun Huang, Yijie Wang
The Many Faces of Adversarial Risk Muni Sreenivas Pydi, Varun Jog
Meta-Adaptive Nonlinear Control: Theory and Algorithms Guanya Shi, Kamyar Azizzadenesheli, Michael O'Connell, Soon-Jo Chung, Yisong Yue
Compositional Reinforcement Learning from Logical Specifications Kishor Jothimurugan, Suguman Bansal, Osbert Bastani, Rajeev Alur
Differentiable Quality Diversity Matthew Fontaine, Stefanos Nikolaidis
Credit Assignment Through Broadcasting a Global Error Vector David Clark, L F Abbott, Sueyeon Chung
An Online Method for A Class of Distributionally Robust Optimization with Non-convex Objectives Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang
A single gradient step finds adversarial examples on random two-layers neural networks Sebastien Bubeck, Yeshwanth Cherapanamjeri, Gauthier Gidel, Remi Tachet des Combes
Parameterized Knowledge Transfer for Personalized Federated Learning Jie Zhang, Song Guo, Xiaosong Ma, Haozhao Wang, Wenchao Xu, Feijie Wu
Contrastively Disentangled Sequential Variational Autoencoder Junwen Bai, Weiran Wang, Carla P. Gomes
Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias Sina Akbari, Ehsan Mokhtarian, AmirEmad Ghassami, Negar Kiyavash
Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime Hugo Cui, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions Bruno Loureiro, Gabriele Sicuro, Cedric Gerbelot, Alessandro Pacco, Florent Krzakala, Lenka Zdeborová
Spectral embedding for dynamic networks with stability guarantees Ian Gallagher, Andrew Jones, Patrick Rubin-Delanchy
Infinite Time Horizon Safety of Bayesian Neural Networks Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee, Thomas Henzinger
Towards understanding retrosynthesis by energy-based models Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, Bo Dai
List-Decodable Mean Estimation in Nearly-PCA Time Ilias Diakonikolas, Daniel Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian
Distributed Zero-Order Optimization under Adversarial Noise Arya Akhavan, Massimiliano Pontil, Alexandre Tsybakov
Reliable Estimation of KL Divergence using a Discriminator in Reproducing Kernel Hilbert Space Sandesh Ghimire, Aria Masoomi, Jennifer Dy
Latent Matters: Learning Deep State-Space Models Alexej Klushyn, Richard Kurle, Maximilian Soelch, Botond Cseke, Patrick van der Smagt
On the Estimation Bias in Double Q-Learning Zhizhou Ren, Guangxiang Zhu, Hao Hu, Beining Han, Jianglun Chen, Chongjie Zhang
Mitigating Forgetting in Online Continual Learning with Neuron Calibration Haiyan Yin, peng yang, Ping Li
Escaping Saddle Points with Compressed SGD Dmitrii Avdiukhin, Grigory Yaroslavtsev
Non-Gaussian Gaussian Processes for Few-Shot Regression Marcin Sendera, Jacek Tabor, Aleksandra Nowak, Andrzej Bedychaj, Massimiliano Patacchiola, Tomasz Trzcinski, Przemysław Spurek, Maciej Zieba
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning Yiqin Yang, Xiaoteng Ma, Chenghao Li, Zewu Zheng, Qiyuan Zhang, Gao Huang, Jun Yang, Qianchuan Zhao
Online Learning in Periodic Zero-Sum Games Tanner Fiez, Ryann Sim, Stratis Skoulakis, Georgios Piliouras, Lillian Ratliff
K-Net: Towards Unified Image Segmentation Wenwei Zhang, Jiangmiao Pang, Kai Chen, Chen Change Loy
Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems Bo Sun, Russell Lee, Mohammad Hajiesmaili, Adam Wierman, Danny Tsang
Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking Zhiyi Ma, Kawin Ethayarajh, Tristan Thrush, Somya Jain, Ledell Wu, Robin Jia, Christopher Potts, Adina Williams, Douwe Kiela
NTopo: Mesh-free Topology Optimization using Implicit Neural Representations Jonas Zehnder, Yue Li, Stelian Coros, Bernhard Thomaszewski
Generalization Bounds for (Wasserstein) Robust Optimization Yang An, Rui Gao
Faster Matchings via Learned Duals Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii
Online learning in MDPs with linear function approximation and bandit feedback. Gergely Neu, Julia Olkhovskaya
Learning Collaborative Policies to Solve NP-hard Routing Problems Minsu Kim, Jinkyoo Park, joungho kim
Efficient Mirror Descent Ascent Methods for Nonsmooth Minimax Problems Feihu Huang, Xidong Wu, Heng Huang
CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum Shuang Ao, Tianyi Zhou, Guodong Long, Qinghua Lu, Liming Zhu, Jing Jiang
Modality-Agnostic Topology Aware Localization Farhad Ghazvinian Zanjani, Ilia Karmanov, Hanno Ackermann, Daniel Dijkman, Simone Merlin, Max Welling, Fatih Porikli
Scalable Quasi-Bayesian Inference for Instrumental Variable Regression Ziyu Wang, Yuhao Zhou, Tongzheng Ren, Jun Zhu
Kernel Identification Through Transformers Fergus Simpson, Ian Davies, Vidhi Lalchand, Alessandro Vullo, Nicolas Durrande, Carl Edward Rasmussen
Curriculum Design for Teaching via Demonstrations: Theory and Applications Gaurav Yengera, Rati Devidze, Parameswaran Kamalaruban, Adish Singla
Revenue maximization via machine learning with noisy data Ellen Vitercik, Tom Yan
Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation Jinming Cui, Chaochao Chen, Lingjuan Lyu, Carl Yang, Wang Li
Parallelizing Thompson Sampling Amin Karbasi, Vahab Mirrokni, Mohammad Shadravan
Dynamic Causal Bayesian Optimization Virginia Aglietti, Neil Dhir, Javier González, Theodoros Damoulas
Local Differential Privacy for Regret Minimization in Reinforcement Learning Evrard Garcelon, Vianney Perchet, Ciara Pike-Burke, Matteo Pirotta
Emergent Discrete Communication in Semantic Spaces Mycal Tucker, Huao Li, Siddharth Agrawal, Dana Hughes, Katia Sycara, Michael Lewis, Julie A Shah
Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity Ran Liu, Mehdi Azabou, Max Dabagia, Chi-Heng Lin, Mohammad Gheshlaghi Azar, Keith Hengen, Michal Valko, Eva Dyer
Equivariant Manifold Flows Isay Katsman, Aaron Lou, Derek Lim, Qingxuan Jiang, Ser Nam Lim, Christopher M De Sa
Scalable Bayesian GPFA with automatic relevance determination and discrete noise models Kristopher Jensen, Ta-Chu Kao, Jasmine Stone, Guillaume Hennequin
Recurrence along Depth: Deep Convolutional Neural Networks with Recurrent Layer Aggregation Jingyu Zhao, Yanwen Fang, Guodong Li
Independent Prototype Propagation for Zero-Shot Compositionality Frank Ruis, Gertjan Burghouts, Doina Bucur
Universal Graph Convolutional Networks Di Jin, Zhizhi Yu, Cuiying Huo, Rui Wang, Xiao Wang, Dongxiao He, Jiawei Han
Adversarial Feature Desensitization Pouya Bashivan, Reza Bayat, Adam Ibrahim, Kartik Ahuja, Mojtaba Faramarzi, Touraj Laleh, Blake Richards, Irina Rish
Few-Shot Data-Driven Algorithms for Low Rank Approximation Piotr Indyk, Tal Wagner, David Woodruff
Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition Mark Boss, Varun Jampani, Raphael Braun, Ce Liu, Jonathan Barron, Hendrik PA Lensch
Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection Morgane Austern, Vasilis Syrgkanis
Dynamic influence maximization Binghui Peng
Risk Monotonicity in Statistical Learning Zakaria Mhammedi
Information is Power: Intrinsic Control via Information Capture Nicholas Rhinehart, Jenny Wang, Glen Berseth, John Co-Reyes, Danijar Hafner, Chelsea Finn, Sergey Levine
Extracting Deformation-Aware Local Features by Learning to Deform Guilherme Potje, Renato Martins, Felipe Chamone, Erickson Nascimento
Object-Centric Representation Learning with Generative Spatial-Temporal Factorization Nanbo Li, Muhammad Ahmed Raza, Wenbin Hu, Zhaole Sun, Robert Fisher
Learning to Simulate Self-driven Particles System with Coordinated Policy Optimization Zhenghao Peng, Quanyi Li, Ka Ming Hui, Chunxiao Liu, Bolei Zhou
Gradient-based Hyperparameter Optimization Over Long Horizons Paul Micaelli, Amos J. Storkey
Stochastic Bias-Reduced Gradient Methods Hilal Asi, Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford
The Causal-Neural Connection: Expressiveness, Learnability, and Inference Kevin Xia, Kai-Zhan Lee, Yoshua Bengio, Elias Bareinboim
Validation Free and Replication Robust Volume-based Data Valuation Xinyi Xu, Zhaoxuan Wu, Chuan Sheng Foo, Bryan Kian Hsiang Low
Implicit Finite-Horizon Approximation and Efficient Optimal Algorithms for Stochastic Shortest Path Liyu Chen, Mehdi Jafarnia-Jahromi, Rahul Jain, Haipeng Luo
A Separation Result Between Data-oblivious and Data-aware Poisoning Attacks Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Guha Thakurta
Deep Learning Through the Lens of Example Difficulty Robert Baldock, Hartmut Maennel, Behnam Neyshabur
R-Drop: Regularized Dropout for Neural Networks xiaobo liang, Lijun Wu, Juntao Li, Yue Wang, Qi Meng, Tao Qin, Wei Chen, Min Zhang, Tie-Yan Liu
Diversity Enhanced Active Learning with Strictly Proper Scoring Rules Wei Tan, Lan Du, Wray Buntine
SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning Sungmin Cha, beomyoung kim, YoungJoon Yoo, Taesup Moon
Lower and Upper Bounds on the Pseudo-Dimension of Tensor Network Models Behnoush Khavari, Guillaume Rabusseau
What Makes Multi-Modal Learning Better than Single (Provably) Yu Huang, Chenzhuang Du, Zihui Xue, Xuanyao Chen, Hang Zhao, Longbo Huang
Quantifying and Improving Transferability in Domain Generalization Guojun Zhang, Han Zhao, Yaoliang Yu, Pascal Poupart
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification Youngseog Chung, Willie Neiswanger, Ian Char, Jeff G. Schneider
Dynamic Inference with Neural Interpreters Nasim Rahaman, Muhammad Waleed Gondal, Shruti Joshi, Peter V. Gehler, Yoshua Bengio, Francesco Locatello, Bernhard Schölkopf
Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces Kirill Struminsky, Artyom Gadetsky, Denis Rakitin, Danil Karpushkin, Dmitry P Vetrov
Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling Greg Ver Steeg, Aram Galstyan
Dynamic Normalization and Relay for Video Action Recognition Dongqi Cai, Anbang Yao, Yurong Chen
Robust Visual Reasoning via Language Guided Neural Module Networks Arjun Akula, Varun Jampani, Soravit Changpinyo, Song-Chun Zhu
True Few-Shot Learning with Language Models Ethan Perez, Douwe Kiela, Kyunghyun Cho
Selective Sampling for Online Best-arm Identification Romain Camilleri, Zhihan Xiong, Maryam Fazel, Lalit Jain, Kevin G. Jamieson
Multi-task Learning of Order-Consistent Causal Graphs Xinshi Chen, Haoran Sun, Caleb Ellington, Eric P. Xing, Le Song
Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, Jing Tang
Learning interaction rules from multi-animal trajectories via augmented behavioral models Keisuke Fujii, Naoya Takeishi, Kazushi Tsutsui, Emyo Fujioka, Nozomi Nishiumi, Ryoya Tanaka, Mika Fukushiro, Kaoru Ide, Hiroyoshi Kohno, Ken Yoda, Susumu Takahashi, Shizuko Hiryu, Yoshinobu Kawahara
Differentiable Synthesis of Program Architectures Guofeng Cui, He Zhu
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications Leonard Berrada, Sumanth Dathathri, Krishnamurthy Dvijotham, Robert Stanforth, Rudy R Bunel, Jonathan Uesato, Sven Gowal, M. Pawan Kumar
Oracle-Efficient Regret Minimization in Factored MDPs with Unknown Structure Aviv Rosenberg, Yishay Mansour
Linear-Time Probabilistic Solution of Boundary Value Problems Nicholas Krämer, Philipp Hennig
Lifelong Domain Adaptation via Consolidated Internal Distribution Mohammad Rostami
Counterbalancing Learning and Strategic Incentives in Allocation Markets Jamie Kang, Faidra Monachou, Moran Koren, Itai Ashlagi
Controlling Neural Networks with Rule Representations Sungyong Seo, Sercan Arik, Jinsung Yoon, Xiang Zhang, Kihyuk Sohn, Tomas Pfister
Making the most of your day: online learning for optimal allocation of time Etienne Boursier, Tristan Garrec, Vianney Perchet, Marco Scarsini
Federated Reconstruction: Partially Local Federated Learning Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, John Rush, Sushant Prakash
Optimal prediction of Markov chains with and without spectral gap Yanjun Han, Soham Jana, Yihong Wu
Subquadratic Overparameterization for Shallow Neural Networks ChaeHwan Song, Ali Ramezani-Kebrya, Thomas Pethick, Armin Eftekhari, Volkan Cevher
Continuous Doubly Constrained Batch Reinforcement Learning Rasool Fakoor, Jonas W Mueller, Kavosh Asadi, Pratik Chaudhari, Alexander J Smola
Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators Qitian Wu, Rui Gao, Hongyuan Zha
Score-based Generative Modeling in Latent Space Arash Vahdat, Karsten Kreis, Jan Kautz
Deep Conditional Gaussian Mixture Model for Constrained Clustering Laura Manduchi, Kieran Chin-Cheong, Holger Michel, Sven Wellmann, Julia Vogt
Bootstrap Your Object Detector via Mixed Training Mengde Xu, Zheng Zhang, Fangyun Wei, Yutong Lin, Yue Cao, Stephen Lin, Han Hu, Xiang Bai
Tensor decompositions of higher-order correlations by nonlinear Hebbian plasticity Gabriel Ocker, Michael Buice
Online Adaptation to Label Distribution Shift Ruihan Wu, Chuan Guo, Yi Su, Kilian Q. Weinberger
One Explanation is Not Enough: Structured Attention Graphs for Image Classification Vivswan Shitole, Fuxin Li, Minsuk Kahng, Prasad Tadepalli, Alan Fern
Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression Zhaozhi Qian, William Zame, Lucas Fleuren, Paul Elbers, Mihaela van der Schaar
Shifted Chunk Transformer for Spatio-Temporal Representational Learning Xuefan Zha, Wentao Zhu, Lv Xun, Sen Yang, Ji Liu
Faster proximal algorithms for matrix optimization using Jacobi-based eigenvalue methods Hamza Fawzi, Harry Goulbourne
Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP Josh Rozner, Christopher Potts, Kyle Mahowald
An Improved Analysis of Gradient Tracking for Decentralized Machine Learning Anastasiia Koloskova, Tao Lin, Sebastian U Stich
Entropic Desired Dynamics for Intrinsic Control Steven Hansen, Guillaume Desjardins, Kate Baumli, David Warde-Farley, Nicolas Heess, Simon Osindero, Volodymyr Mnih
Exploring Cross-Video and Cross-Modality Signals for Weakly-Supervised Audio-Visual Video Parsing Yan-Bo Lin, Hung-Yu Tseng, Hsin-Ying Lee, Yen-Yu Lin, Ming-Hsuan Yang
Littlestone Classes are Privately Online Learnable Noah Golowich, Roi Livni
Dual Parameterization of Sparse Variational Gaussian Processes Vincent ADAM, Paul Chang, Mohammad Emtiyaz Khan, Arno Solin
Learning to dehaze with polarization Chu Zhou, Minggui Teng, Yufei Han, Chao Xu, Boxin Shi
Conservative Data Sharing for Multi-Task Offline Reinforcement Learning Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Sergey Levine, Chelsea Finn
Universal Rate-Distortion-Perception Representations for Lossy Compression George Zhang, Jingjing Qian, Jun Chen, Ashish Khisti
What’s a good imputation to predict with missing values? Marine Le Morvan, Julie Josse, Erwan Scornet, Gael Varoquaux
Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification Ben Eysenbach, Sergey Levine, Ruslan Salakhutdinov
Hierarchical Skills for Efficient Exploration Jonas Gehring, Gabriel Synnaeve, Andreas Krause, Nicolas Usunier
Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models Phil Chen, Mikhal Itkina, Ransalu Senanayake, Mykel J Kochenderfer
Submodular + Concave Siddharth Mitra, Moran Feldman, Amin Karbasi
DeepGEM: Generalized Expectation-Maximization for Blind Inversion Angela Gao, Jorge Castellanos, Yisong Yue, Zachary Ross, Katherine Bouman
Learning to Generate Visual Questions with Noisy Supervision Shen Kai, Lingfei Wu, Siliang Tang, Yueting Zhuang, zhen he, Zhuoye Ding, Yun Xiao, Bo Long
Pure Exploration in Kernel and Neural Bandits Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang, Quanquan Gu, Rebecca Willett, Robert Nowak
Numerical Composition of Differential Privacy Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
Coresets for Classification – Simplified and Strengthened Tung Mai, Cameron Musco, Anup Rao
Sequential Algorithms for Testing Closeness of Distributions Aadil Oufkir, Omar Fawzi, Nicolas Flammarion, Aurélien Garivier
Overlapping Spaces for Compact Graph Representations Kirill Shevkunov, Liudmila Prokhorenkova
Hyperparameter Tuning is All You Need for LISTA Xiaohan Chen, Jialin Liu, Zhangyang Wang, Wotao Yin
Foundations of Symbolic Languages for Model Interpretability Marcelo Arenas, Daniel Báez, Pablo Barceló, Jorge Pérez, Bernardo Subercaseaux
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism Paria Rashidinejad, Banghua Zhu, Cong Ma, Jiantao Jiao, Stuart J. Russell
Impression learning: Online representation learning with synaptic plasticity Colin Bredenberg, Benjamin Lyo, Eero P. Simoncelli, Cristina Savin
How Well do Feature Visualizations Support Causal Understanding of CNN Activations? Roland S. Zimmermann, Judy Borowski, Robert Geirhos, Matthias Bethge, Thomas Wallis, Wieland Brendel
Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems Ruihan Wu, Chuan Guo, Awni Hannun, Laurens van der Maaten
Coarse-to-fine Animal Pose and Shape Estimation Chen Li, Gim Hee Lee
Meta-Learning Sparse Implicit Neural Representations Jaeho Lee, Jihoon Tack, Namhoon Lee, Jinwoo Shin
Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang
Sparse Spiking Gradient Descent Nicolas Perez-Nieves, Dan Goodman
Rethinking Calibration of Deep Neural Networks: Do Not Be Afraid of Overconfidence Deng-Bao Wang, Lei Feng, Min-Ling Zhang
Towards Efficient and Effective Adversarial Training Gaurang Sriramanan, Sravanti Addepalli, Arya Baburaj, Venkatesh Babu R
Intriguing Properties of Contrastive Losses Ting Chen, Calvin Luo, Lala Li
Detecting Moments and Highlights in Videos via Natural Language Queries Jie Lei, Tamara L Berg, Mohit Bansal
Stochastic optimization under time drift: iterate averaging, step-decay schedules, and high probability guarantees Joshua Cutler, Dmitriy Drusvyatskiy, Zaid Harchaoui
Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems Andreas Schlaginhaufen, Philippe Wenk, Andreas Krause, Florian Dorfler
An Uncertainty Principle is a Price of Privacy-Preserving Microdata John Abowd, Robert Ashmead, Ryan Cumings-Menon, Simson Garfinkel, Daniel Kifer, Philip Leclerc, William Sexton, Ashley Simpson, Christine Task, Pavel Zhuravlev
Fairness in Ranking under Uncertainty Ashudeep Singh, David Kempe, Thorsten Joachims
Generalized Proximal Policy Optimization with Sample Reuse James Queeney, Yannis Paschalidis, Christos G Cassandras
Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data Gongfan Fang, Yifan Bao, Jie Song, Xinchao Wang, Donglin Xie, Chengchao Shen, Mingli Song
Batch Active Learning at Scale Gui Citovsky, Giulia DeSalvo, Claudio Gentile, Lazaros Karydas, Anand Rajagopalan, Afshin Rostamizadeh, Sanjiv Kumar
Joint Semantic Mining for Weakly Supervised RGB-D Salient Object Detection Jingjing Li, Wei Ji, Qi Bi, Cheng Yan, Miao Zhang, Yongri Piao, Huchuan Lu, Li cheng
Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition Yulin Wang, Rui Huang, Shiji Song, Zeyi Huang, Gao Huang
Contrastive Learning for Neural Topic Model Thong Nguyen, Anh Tuan Luu
Learning in two-player zero-sum partially observable Markov games with perfect recall Tadashi Kozuno, Pierre Ménard, Remi Munos, Michal Valko
A Geometric Structure of Acceleration and Its Role in Making Gradients Small Fast Jongmin Lee, Chanwoo Park, Ernest Ryu
ATISS: Autoregressive Transformers for Indoor Scene Synthesis Despoina Paschalidou, Amlan Kar, Maria Shugrina, Karsten Kreis, Andreas Geiger, Sanja Fidler
Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks Hassan Dbouk, Naresh Shanbhag
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning Christoph Dann, Mehryar Mohri, Tong Zhang, Julian Zimmert
Fast Federated Learning in the Presence of Arbitrary Device Unavailability Xinran Gu, Kaixuan Huang, Jingzhao Zhang, Longbo Huang
On The Structure of Parametric Tournaments with Application to Ranking from Pairwise Comparisons Vishnu Veerathu, Arun Rajkumar
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo
Fairness via Representation Neutralization Mengnan Du, Subhabrata Mukherjee, Guanchu Wang, Ruixiang Tang, Ahmed Awadallah, Xia Hu
Residual Relaxation for Multi-view Representation Learning Yifei Wang, Zhengyang Geng, Feng Jiang, Chuming Li, Yisen Wang, Jiansheng Yang, Zhouchen Lin
Do Vision Transformers See Like Convolutional Neural Networks? Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, Alexey Dosovitskiy
Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning Ali Taghibakhshi, Scott MacLachlan, Luke Olson, Matthew West
Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems Wenqing Zheng, Qiangqiang Guo, Hao Yang, Peihao Wang, Zhangyang Wang
Explaining Latent Representations with a Corpus of Examples Jonathan Crabbe, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar
Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks Aran Nayebi, Alexander Attinger, Malcolm Campbell, Kiah Hardcastle, Isabel Low, Caitlin S Mallory, Gabriel Mel, Ben Sorscher, Alex H Williams, Surya Ganguli, Lisa Giocomo, Dan Yamins
Beyond Smoothness: Incorporating Low-Rank Analysis into Nonparametric Density Estimation Robert A Vandermeulen, Antoine Ledent
Multi-View Representation Learning via Total Correlation Objective HyeongJoo Hwang, Geon-Hyeong Kim, Seunghoon Hong, Kee-Eung Kim
FACMAC: Factored Multi-Agent Centralised Policy Gradients Bei Peng, Tabish Rashid, Christian Schroeder de Witt, Pierre-Alexandre Kamienny, Philip Torr, Wendelin Boehmer, Shimon Whiteson
EDGE: Explaining Deep Reinforcement Learning Policies Wenbo Guo, Xian Wu, Usmann Khan, Xinyu Xing
Learning to Assimilate in Chaotic Dynamical Systems Michael McCabe, Jed Brown
Object-aware Contrastive Learning for Debiased Scene Representation Sangwoo Mo, Hyunwoo Kang, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin
Evaluating Efficient Performance Estimators of Neural Architectures Xuefei Ning, Changcheng Tang, Wenshuo Li, Zixuan Zhou, Shuang Liang, Huazhong Yang, Yu Wang
A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose Shih-Yang Su, Frank Yu, Michael Zollhoefer, Helge Rhodin
Differential Privacy Over Riemannian Manifolds Matthew Reimherr, Karthik Bharath, Carlos Soto
How can classical multidimensional scaling go wrong? Rishi Sonthalia, Greg Van Buskirk, Benjamin Raichel, Anna Gilbert
Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones Yushi Bai, Zhitao Ying, Hongyu Ren, Jure Leskovec
Non-asymptotic Error Bounds for Bidirectional GANs Shiao Liu, Yunfei Yang, Jian Huang, Yuling Jiao, Yang Wang
Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality Songyuan Zhang, ZHANGJIE CAO, Dorsa Sadigh, Yanan Sui
Answering Complex Causal Queries With the Maximum Causal Set Effect Zachary Markovich
Identifiability in inverse reinforcement learning Haoyang Cao, Samuel Cohen, Lukasz Szpruch
A Probabilistic State Space Model for Joint Inference from Differential Equations and Data Jonathan Schmidt, Nicholas Krämer, Philipp Hennig
On Plasticity, Invariance, and Mutually Frozen Weights in Sequential Task Learning Julian Zilly, Alessandro Achille, Andrea Censi, Emilio Frazzoli
Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning Guanlin Liu, Lifeng LAI
Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections Kimia Nadjahi, Alain Durmus, Pierre E Jacob, Roland Badeau, Umut Simsekli
Causal Navigation by Continuous-time Neural Networks Charles Vorbach, Ramin Hasani, Alexander Amini, Mathias Lechner, Daniela Rus
Global Convergence of Online Optimization for Nonlinear Model Predictive Control Sen Na
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling
Learning with User-Level Privacy Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh
Don’t Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence Tianshi Cao, Alex Bie, Arash Vahdat, Sanja Fidler, Karsten Kreis
Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers Mandela Patrick, Dylan Campbell, Yuki Asano, Ishan Misra, Florian Metze, Christoph Feichtenhofer, Andrea Vedaldi, João F. Henriques
Variational Bayesian Optimistic Sampling Brendan O'Donoghue, Tor Lattimore
Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement Learning Xiong-Hui Chen, Shengyi Jiang, Feng Xu, Zongzhang Zhang, Yang Yu
D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation Abhishek Sinha, Jiaming Song, Chenlin Meng, Stefano Ermon
Continual Auxiliary Task Learning Matthew McLeod, Chunlok Lo, Matthew Schlegel, Andrew Jacobsen, Raksha Kumaraswamy, Martha White, Adam White
Constrained Two-step Look-Ahead Bayesian Optimization Yunxiang Zhang, Xiangyu Zhang, Peter Frazier
Learning with Labeling Induced Abstentions Kareem Amin, Giulia DeSalvo, Afshin Rostamizadeh
SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning Mattia Atzeni, Jasmina Bogojeska, Andreas Loukas
Out-of-Distribution Generalization in Kernel Regression Abdulkadir Canatar, Blake Bordelon, Cengiz Pehlevan
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective Jingwei Sun, Ang Li, Louis DiValentin, Amin Hassanzadeh, Yiran Chen, Hai Li
Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote Yi-Shan Wu, Andres Masegosa, Stephan Lorenzen, Christian Igel, Yevgeny Seldin
A Multi-Implicit Neural Representation for Fonts Pradyumna Reddy, Zhifei Zhang, Zhaowen Wang, Matthew Fisher, Hailin Jin, Niloy Mitra
OctField: Hierarchical Implicit Functions for 3D Modeling Jia-Heng Tang, Weikai Chen, jie Yang, Bo Wang, Songrun Liu, Bo Yang, Lin Gao
The Inductive Bias of Quantum Kernels Jonas Kübler, Simon Buchholz, Bernhard Schölkopf
An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks Shashank Rajput, Kartik Sreenivasan, Dimitris Papailiopoulos, Amin Karbasi
Pretraining Representations for Data-Efficient Reinforcement Learning Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R Devon Hjelm, Philip Bachman, Aaron C. Courville
Universal Approximation Using Well-Conditioned Normalizing Flows Holden Lee, Chirag Pabbaraju, Anish Prasad Sevekari, Andrej Risteski
On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs) Zhiyuan Li, Sadhika Malladi, Sanjeev Arora
Proportional Participatory Budgeting with Additive Utilities Dominik Peters, Grzegorz Pierczyński, Piotr Skowron
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect Lorenzo Noci, Kevin Roth, Gregor Bachmann, Sebastian Nowozin, Thomas Hofmann
Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot? Xiaolong Ma, Geng Yuan, Xuan Shen, Tianlong Chen, Xuxi Chen, Xiaohan Chen, Ning Liu, Minghai Qin, Sijia Liu, Zhangyang Wang, Yanzhi Wang
Collaborative Causal Discovery with Atomic Interventions Raghavendra Addanki, Shiva P. Kasiviswanathan
Towards optimally abstaining from prediction with OOD test examples Adam Kalai, Varun Kanade
TokenLearner: Adaptive Space-Time Tokenization for Videos Michael Ryoo, AJ Piergiovanni, Anurag Arnab, Mostafa Dehghani, Anelia Angelova
Learning in Multi-Stage Decentralized Matching Markets Xiaowu Dai, Michael I. Jordan
Non-asymptotic convergence bounds for Wasserstein approximation using point clouds Quentin Mérigot, Filippo Santambrogio, Clément SARRAZIN
Understanding Interlocking Dynamics of Cooperative Rationalization Mo Yu, Yang Zhang, Shiyu Chang, Tommi Jaakkola
Adversarial Robustness without Adversarial Training: A Teacher-Guided Curriculum Learning Approach Anindya Sarkar, Anirban Sarkar, Sowrya Gali, Vineeth N Balasubramanian
Tactical Optimism and Pessimism for Deep Reinforcement Learning Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel, Michael I. Jordan
Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning Siyuan Zhang, Nan Jiang
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout Samuel Horváth, Stefanos Laskaridis, Mario Almeida, Ilias Leontiadis, Stylianos Venieris, Nicholas Lane
Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings Ming Yin, Yu-Xiang Wang
MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data Zhibo Zhu, Ziqi Liu, Ge Jin, Zhiqiang Zhang, Lei Chen, Jun Zhou, Jianyong Zhou
Pareto Domain Adaptation fangrui lv, Jian Liang, Kaixiong Gong, Shuang Li, Chi Harold Liu, Han Li, Di Liu, Guoren Wang
Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals Lang Liu, Krishna Pillutla, Sean Welleck, Sewoong Oh, Yejin Choi, Zaid Harchaoui
Consistency Regularization for Variational Auto-Encoders Samarth Sinha, Adji Bousso Dieng
Interactive Label Cleaning with Example-based Explanations Stefano Teso, Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini
Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias Kaifeng Lyu, Zhiyuan Li, Runzhe Wang, Sanjeev Arora
Glance-and-Gaze Vision Transformer Qihang Yu, Yingda Xia, Yutong Bai, Yongyi Lu, Alan L. Yuille, Wei Shen
Stochastic $L^\natural$-convex Function Minimization Haixiang Zhang, Zeyu Zheng, Javad Lavaei
Self-Supervised GANs with Label Augmentation Liang Hou, Huawei Shen, Qi Cao, Xueqi Cheng
Shape As Points: A Differentiable Poisson Solver Songyou Peng, Chiyu Jiang, Yiyi Liao, Michael Niemeyer, Marc Pollefeys, Andreas Geiger
Outcome-Driven Reinforcement Learning via Variational Inference Tim G. J. Rudner, Vitchyr Pong, Rowan McAllister, Yarin Gal, Sergey Levine
Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks Yonggan Fu, Qixuan Yu, Yang Zhang, Shang Wu, Xu Ouyang, David Cox, Yingyan Lin
Rectifying the Shortcut Learning of Background for Few-Shot Learning Xu Luo, Longhui Wei, Liangjian Wen, Jinrong Yang, Lingxi Xie, Zenglin Xu, Qi Tian
SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency Devendra Singh Chaplot, Murtaza Dalal, Saurabh Gupta, Jitendra Malik, Ruslan Salakhutdinov
Sifting through the noise: Universal first-order methods for stochastic variational inequalities Kimon Antonakopoulos, Thomas Pethick, Ali Kavis, Panayotis Mertikopoulos, Volkan Cevher
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning Jingfeng Wu, Vladimir Braverman, Lin Yang
Exact Privacy Guarantees for Markov Chain Implementations of the Exponential Mechanism with Artificial Atoms Jeremy Seeman, Matthew Reimherr, Aleksandra Slavković
The Emergence of Objectness: Learning Zero-shot Segmentation from Videos Runtao Liu, Zhirong Wu, Stella X. Yu, Stephen Lin
Direct Multi-view Multi-person 3D Pose Estimation tao wang, Jianfeng Zhang, Yujun Cai, Shuicheng Yan, Jiashi Feng
MST: Masked Self-Supervised Transformer for Visual Representation Zhaowen Li, Zhiyang Chen, Fan Yang, Wei Li, Yousong Zhu, Chaoyang Zhao, Rui Deng, Liwei Wu, Rui Zhao, Ming Tang, Jinqiao Wang
Exploiting Opponents Under Utility Constraints in Sequential Games Martino Bernasconi-de-Luca, Federico Cacciamani, Simone Fioravanti, Nicola Gatti, Alberto Marchesi, Francesco Trovò
A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference Antonio Vergari, YooJung Choi, Anji Liu, Stefano Teso, Guy Van den Broeck
Demystifying and Generalizing BinaryConnect Tim Dockhorn, Yaoliang Yu, Eyyüb Sari, Mahdi Zolnouri, Vahid Partovi Nia
CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator Alek Dimitriev, Mingyuan Zhou
Learning to Learn Dense Gaussian Processes for Few-Shot Learning Ze Wang, Zichen Miao, Xiantong Zhen, Qiang Qiu
Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser Zahra Kadkhodaie, Eero P. Simoncelli
Towards Stable and Robust AdderNets Minjing Dong, Yunhe Wang, Xinghao Chen, Chang Xu
Representing Long-Range Context for Graph Neural Networks with Global Attention Zhanghao Wu, Paras Jain, Matthew Wright, Azalia Mirhoseini, Joseph E Gonzalez, Ion Stoica
Beyond Bandit Feedback in Online Multiclass Classification Dirk van der Hoeven, Federico Fusco, Nicolò Cesa-Bianchi
Learning Student-Friendly Teacher Networks for Knowledge Distillation Dae Young Park, Moon-Hyun Cha, changwook jeong, Daesin Kim, Bohyung Han
Implicit Transformer Network for Screen Content Image Continuous Super-Resolution Jingyu Yang, Sheng Shen, Huanjing Yue, Kun Li
Channel Permutations for N:M Sparsity Jeff Pool, Chong Yu
Curriculum Learning for Vision-and-Language Navigation Jiwen Zhang, zhongyu wei, Jianqing Fan, Jiajie Peng
Better Algorithms for Individually Fair $k$-Clustering Maryam Negahbani, Deeparnab Chakrabarty
Video Instance Segmentation using Inter-Frame Communication Transformers Sukjun Hwang, Miran Heo, Seoung Wug Oh, Seon Joo Kim
Progressive Coordinate Transforms for Monocular 3D Object Detection Li Wang, Li Zhang, Yi Zhu, Zhi Zhang, Tong He, Mu Li, Xiangyang Xue
Structured Reordering for Modeling Latent Alignments in Sequence Transduction bailin wang, Mirella Lapata, Ivan Titov
A universal probabilistic spike count model reveals ongoing modulation of neural variability David Liu, Mate Lengyel
Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms Chi Jin, Qinghua Liu, Sobhan Miryoosefi
Detecting Anomalous Event Sequences with Temporal Point Processes Oleksandr Shchur, Ali Caner Turkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann
HNPE: Leveraging Global Parameters for Neural Posterior Estimation Pedro Rodrigues, Thomas Moreau, Gilles Louppe, Alexandre Gramfort
Alignment Attention by Matching Key and Query Distributions Shujian Zhang, Xinjie Fan, Huangjie Zheng, Korawat Tanwisuth, Mingyuan Zhou
Settling the Variance of Multi-Agent Policy Gradients Jakub Grudzien Kuba, Muning Wen, Linghui Meng, shangding gu, Haifeng Zhang, David Mguni, Jun Wang, Yaodong Yang
For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets Brian Trippe, Hilary Finucane, Tamara Broderick
Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan
Controllable and Compositional Generation with Latent-Space Energy-Based Models Weili Nie, Arash Vahdat, Anima Anandkumar
Reverse-Complement Equivariant Networks for DNA Sequences Vincent Mallet, Jean-Philippe Vert
Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints Tianhao Wang, Dongruo Zhou, Quanquan Gu
Nonsmooth Implicit Differentiation for Machine-Learning and Optimization Jérôme Bolte, Tam Le, Edouard Pauwels, Tony Silveti-Falls
Heuristic-Guided Reinforcement Learning Ching-An Cheng, Andrey Kolobov, Adith Swaminathan
Statistical Undecidability in Linear, Non-Gaussian Causal Models in the Presence of Latent Confounders Konstantin Genin
A novel notion of barycenter for probability distributions based on optimal weak mass transport Elsa Cazelles, Felipe Tobar, Joaquin Fontbona
Temporal-attentive Covariance Pooling Networks for Video Recognition Zilin Gao, Qilong Wang, Bingbing Zhang, Qinghua Hu, Peihua Li
Revisiting Smoothed Online Learning Lijun Zhang, Wei Jiang, Shiyin Lu, Tianbao Yang
Marginalised Gaussian Processes with Nested Sampling Fergus Simpson, Vidhi Lalchand, Carl Edward Rasmussen
Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning Andrea Zanette, Martin J Wainwright, Emma Brunskill
Bayesian Bellman Operators Mattie Fellows, Kristian Hartikainen, Shimon Whiteson
Uncertainty Calibration for Ensemble-Based Debiasing Methods Ruibin Xiong, Yimeng Chen, Liang Pang, Xueqi Cheng, Zhi-Ming Ma, Yanyan Lan
Provably Faster Algorithms for Bilevel Optimization Junjie Yang, Kaiyi Ji, Yingbin Liang
Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction Seongjun Yun, Seoyoon Kim, Junhyun Lee, Jaewoo Kang, Hyunwoo J Kim
Self-Supervised Multi-Object Tracking with Cross-input Consistency Favyen Bastani, Songtao He, Samuel Madden
Tree in Tree: from Decision Trees to Decision Graphs Bingzhao Zhu, Mahsa Shoaran
Test-time Collective Prediction Celestine Mendler-Dünner, Wenshuo Guo, Stephen Bates, Michael I. Jordan
A Continuous Mapping For Augmentation Design Keyu Tian, Chen Lin, Ser Nam Lim, Wanli Ouyang, Puneet Dokania, Philip Torr
Neural Routing by Memory Kaipeng Zhang, Zhenqiang Li, Zhifeng Li, Wei Liu, Yoichi Sato
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles Octavian Ganea, Lagnajit Pattanaik, Connor Coley, Regina Barzilay, Klavs Jensen, William Green, Tommi Jaakkola
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression Zhize Li, Peter Richtarik
Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers Mikita Dvornik, Isma Hadji, Konstantinos G. Derpanis, Animesh Garg, Allan D. Jepson
Safe Reinforcement Learning with Natural Language Constraints Tsung-Yen Yang, Michael Y Hu, Yinlam Chow, Peter J Ramadge, Karthik Narasimhan
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features Thomas McDonald, Mauricio Álvarez
Implicit Semantic Response Alignment for Partial Domain Adaptation Wenxiao Xiao, Zhengming Ding, Hongfu Liu
ToAlign: Task-Oriented Alignment for Unsupervised Domain Adaptation Guoqiang Wei, Cuiling Lan, Wenjun Zeng, Zhizheng Zhang, Zhibo Chen
Prior-independent Dynamic Auctions for a Value-maximizing Buyer Yuan Deng, Hanrui Zhang
Safe Reinforcement Learning by Imagining the Near Future Garrett Thomas, Yuping Luo, Tengyu Ma
Contrastive Active Inference Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt
Overparameterization Improves Robustness to Covariate Shift in High Dimensions Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington
Logarithmic Regret in Feature-based Dynamic Pricing Jianyu Xu, Yu-Xiang Wang
Dimension-free empirical entropy estimation Doron Cohen, Aryeh Kontorovich, Aaron Koolyk, Geoffrey Wolfer
Towards Biologically Plausible Convolutional Networks Roman Pogodin, Yash Mehta, Timothy Lillicrap, Peter E Latham
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, Cho-Jui Hsieh
Learning Transferable Adversarial Perturbations Krishna kanth Nakka, Mathieu Salzmann
PortaSpeech: Portable and High-Quality Generative Text-to-Speech Yi Ren, Jinglin Liu, Zhou Zhao
Exponential Graph is Provably Efficient for Decentralized Deep Training Bicheng Ying, Kun Yuan, Yiming Chen, Hanbin Hu, PAN PAN, Wotao Yin
CLIP-It! Language-Guided Video Summarization Medhini Narasimhan, Anna Rohrbach, Trevor Darrell
Learning Treatment Effects in Panels with General Intervention Patterns Vivek Farias, Andrew Li, Tianyi Peng
Lossy Compression for Lossless Prediction Yann Dubois, Benjamin Bloem-Reddy, Karen Ullrich, Chris J Maddison
From Optimality to Robustness: Adaptive Re-Sampling Strategies in Stochastic Bandits Dorian Baudry, Patrick Saux, Odalric-Ambrym Maillard
CCVS: Context-aware Controllable Video Synthesis Guillaume Le Moing, Jean Ponce, Cordelia Schmid
An Online Riemannian PCA for Stochastic Canonical Correlation Analysis Zihang Meng, Rudrasis Chakraborty, Vikas Singh
Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamics Bhavin Choksi, Milad Mozafari, Callum Biggs O'May, B. ADOR, Andrea Alamia, Rufin VanRullen
Deep Extrapolation for Attribute-Enhanced Generation Alvin Chan, Ali Madani, Ben Krause, Nikhil Naik
Generalized DataWeighting via Class-Level Gradient Manipulation Can Chen, Shuhao Zheng, Xi Chen, Erqun Dong, Xue (Steve) Liu, Hao Liu, Dejing Dou
Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation Can Qin, Handong Zhao, Lichen Wang, Huan Wang, Yulun Zhang, Yun Fu
Meta Learning Backpropagation And Improving It Louis Kirsch, Jürgen Schmidhuber
Posterior Meta-Replay for Continual Learning Christian Henning, Maria Cervera, Francesco D'Angelo, Johannes von Oswald, Regina Traber, Benjamin Ehret, Seijin Kobayashi, Benjamin F. Grewe, João Sacramento
Optimizing Reusable Knowledge for Continual Learning via Metalearning Julio Hurtado, Alain Raymond, Alvaro Soto
A sampling-based circuit for optimal decision making Camille Rullán Buxó, Cristina Savin
Compressed Video Contrastive Learning Yuqi Huo, Mingyu Ding, Haoyu Lu, Nanyi Fei, Zhiwu Lu, Ji-Rong Wen, Ping Luo
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation Jiafan He, Dongruo Zhou, Quanquan Gu
Attention Bottlenecks for Multimodal Fusion Arsha Nagrani, Shan Yang, Anurag Arnab, Aren Jansen, Cordelia Schmid, Chen Sun
Convergence of adaptive algorithms for constrained weakly convex optimization Ahmet Alacaoglu, Yura Malitsky, Volkan Cevher
On the Convergence of Step Decay Step-Size for Stochastic Optimization Xiaoyu Wang, Sindri Magnússon, Mikael Johansson
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation Mingguo He, Zhewei Wei, zengfeng Huang, Hongteng Xu
Co-evolution Transformer for Protein Contact Prediction He Zhang, Fusong Ju, Jianwei Zhu, Liang He, Bin Shao, Nanning Zheng, Tie-Yan Liu
Unsupervised Foreground Extraction via Deep Region Competition Peiyu Yu, Sirui Xie, Xiaojian Ma, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu
Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation Divyansh Jhunjhunwala, Ankur Mallick, Advait Gadhikar, Swanand Kadhe, Gauri Joshi
Last-iterate Convergence in Extensive-Form Games Chung-Wei Lee, Christian Kroer, Haipeng Luo
Class-Incremental Learning via Dual Augmentation Fei Zhu, Zhen Cheng, Xu-yao Zhang, Cheng-lin Liu
Robust and Fully-Dynamic Coreset for Continuous-and-Bounded Learning (With Outliers) Problems Zixiu Wang, Yiwen Guo, Hu Ding
Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization Guoqiang Wu, Chongxuan LI, Kun Xu, Jun Zhu
Fair Clustering Under a Bounded Cost Seyed Esmaeili, Brian Brubach, Aravind Srinivasan, John Dickerson
Improving Calibration through the Relationship with Adversarial Robustness Yao Qin, Xuezhi Wang, Alex Beutel, Ed Chi
Credal Self-Supervised Learning Julian Lienen, Eyke Hüllermeier
Spot the Difference: Detection of Topological Changes via Geometric Alignment Per Steffen Czolbe, Aasa Feragen, Oswin Krause
Rethinking the Variational Interpretation of Accelerated Optimization Methods Peiyuan Zhang, Antonio Orvieto, Hadi Daneshmand
Linear and Kernel Classification in the Streaming Model: Improved Bounds for Heavy Hitters Arvind Mahankali, David Woodruff
A PAC-Bayes Analysis of Adversarial Robustness Paul Viallard, Eric Guillaume VIDOT, Amaury Habrard, Emilie Morvant
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities Oliver Unke, Mihail Bogojeski, Michael Gastegger, Mario Geiger, Tess Smidt, Klaus-Robert Müller
Modified Frank Wolfe in Probability Space Carson Kent, Jiajin Li, Jose Blanchet, Peter W Glynn
Bayesian Optimization of Function Networks Raul Astudillo, Peter Frazier
Look at What I’m Doing: Self-Supervised Spatial Grounding of Narrations in Instructional Videos Reuben Tan, Bryan Plummer, Kate Saenko, Hailin Jin, Bryan Russell
RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning Krishnateja Killamsetty, Xujiang Zhao, Feng Chen, Rishabh Iyer
Collaborating with Humans without Human Data DJ Strouse, Kevin McKee, Matt Botvinick, Edward Hughes, Richard Everett
Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Yisen Wang, Zhouchen Lin
Online Selective Classification with Limited Feedback Aditya Gangrade, Anil Kag, Ashok Cutkosky, Venkatesh Saligrama
Controlled Text Generation as Continuous Optimization with Multiple Constraints Sachin Kumar, Eric Malmi, Aliaksei Severyn, Yulia Tsvetkov
S$^3$: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks Xinlin Li, Bang Liu, Yaoliang Yu, Wulong Liu, Chunjing XU, Vahid Partovi Nia
Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions Mathias Niepert, Pasquale Minervini, Luca Franceschi
Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification Alkis Gotovos, Rebekka Burkholz, John Quackenbush, Stefanie Jegelka
Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark Alexander Korotin, Lingxiao Li, Aude Genevay, Justin M Solomon, Alexander Filippov, Evgeny Burnaev
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients Aritra Mitra, Rayana Jaafar, George J. Pappas, Hamed Hassani
On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms Shuyu Cheng, Guoqiang Wu, Jun Zhu
Revisit Multimodal Meta-Learning through the Lens of Multi-Task Learning Milad Abdollahzadeh, Touba Malekzadeh, Ngai-Man (Man) Cheung
Dynamic Sasvi: Strong Safe Screening for Norm-Regularized Least Squares Hiroaki Yamada, Makoto Yamada
What Matters for Adversarial Imitation Learning? Manu Orsini, Anton Raichuk, Leonard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Marcin Andrychowicz
Sequential Causal Imitation Learning with Unobserved Confounders Daniel Kumor, Junzhe Zhang, Elias Bareinboim
Topic Modeling Revisited: A Document Graph-based Neural Network Perspective Dazhong Shen, Chuan Qin, Chao Wang, Zheng Dong, Hengshu Zhu, Hui Xiong
Hard-Attention for Scalable Image Classification Athanasios Papadopoulos, Pawel Korus, Nasir Memon
Fast Routing under Uncertainty: Adaptive Learning in Congestion Games via Exponential Weights Dong Quan Vu, Kimon Antonakopoulos, Panayotis Mertikopoulos
Profiling Pareto Front With Multi-Objective Stein Variational Gradient Descent Xingchao Liu, Xin Tong, Qiang Liu
MAP Propagation Algorithm: Faster Learning with a Team of Reinforcement Learning Agents Stephen Chung
TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up Yifan Jiang, Shiyu Chang, Zhangyang Wang
A Central Limit Theorem for Differentially Private Query Answering Jinshuo Dong, Weijie Su, Linjun Zhang
Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent Rishav Chourasia, Jiayuan Ye, Reza Shokri
Data driven semi-supervised learning Maria-Florina F Balcan, Dravyansh Sharma
Online Meta-Learning via Learning with Layer-Distributed Memory Sudarshan Babu, Pedro Savarese, Michael Maire
Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling Naoya Takeishi, Alexandros Kalousis
Characterizing the risk of fairwashing Ulrich Aïvodji, Hiromi Arai, Sébastien Gambs, Satoshi Hara
Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples Kanghyun Choi, Deokki Hong, Noseong Park, Youngsok Kim, Jinho Lee
Embedding Principle of Loss Landscape of Deep Neural Networks Yaoyu Zhang, Zhongwang Zhang, Tao Luo, Zhiqin J Xu
Adversarial Reweighting for Partial Domain Adaptation Xiang Gu, Xi Yu, yan yang, Jian Sun, Zongben Xu
M-FAC: Efficient Matrix-Free Approximations of Second-Order Information Elias Frantar, Eldar Kurtic, Dan Alistarh
Graph Adversarial Self-Supervised Learning Longqi Yang, Liangliang Zhang, Wenjing Yang
Anti-Backdoor Learning: Training Clean Models on Poisoned Data Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li, Xingjun Ma
Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models Keunseo Kim, JunCheol Shin, Heeyoung Kim
Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks Qiyu Kang, Yang Song, Qinxu Ding, Wee Peng Tay
Robust Compressed Sensing MRI with Deep Generative Priors Ajil Jalal, Marius Arvinte, Giannis Daras, ecprice, Alexandros G Dimakis, Jon Tamir
H-NeRF: Neural Radiance Fields for Rendering and Temporal Reconstruction of Humans in Motion Hongyi Xu, Thiemo Alldieck, Cristian Sminchisescu
DOBF: A Deobfuscation Pre-Training Objective for Programming Languages Marie-Anne Lachaux, Baptiste Roziere, Marc Szafraniec, Guillaume Lample
Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles Jiefeng Chen, Frederick Liu, Besim Avci, Xi Wu, Yingyu Liang, Somesh Jha
Exploiting Chain Rule and Bayes' Theorem to Compare Probability Distributions Huangjie Zheng, Mingyuan Zhou
Actively Identifying Causal Effects with Latent Variables Given Only Response Variable Observable Tian-Zuo Wang, Zhi-Hua Zhou
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models Matej Zečević, Devendra Dhami, Athresh Karanam, Sriraam Natarajan, Kristian Kersting
PettingZoo: Gym for Multi-Agent Reinforcement Learning J Terry, Benjamin Black, Nathaniel Grammel, Mario Jayakumar, Ananth Hari, Ryan Sullivan, Luis S Santos, Clemens Dieffendahl, Caroline Horsch, Rodrigo Perez-Vicente, Niall Williams, Yashas Lokesh, Praveen Ravi
Parametric Complexity Bounds for Approximating PDEs with Neural Networks Tanya Marwah, Zachary Lipton, Andrej Risteski
Learning-to-learn non-convex piecewise-Lipschitz functions Maria-Florina F Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar
Uncertain Decisions Facilitate Better Preference Learning Cassidy Laidlaw, Stuart J. Russell
Decision Transformer: Reinforcement Learning via Sequence Modeling Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch
Probability Paths and the Structure of Predictions over Time Zhiyuan Jerry Lin, Hao Sheng, Sharad Goel
Deep Extended Hazard Models for Survival Analysis Qixian Zhong, Jonas W Mueller, Jane-Ling Wang
TNASP: A Transformer-based NAS Predictor with a Self-evolution Framework Shun Lu, Jixiang Li, Jianchao Tan, Sen Yang, Ji Liu
Automorphic Equivalence-aware Graph Neural Network Fengli Xu, Quanming Yao, Pan Hui, Yong Li
Random Shuffling Beats SGD Only After Many Epochs on Ill-Conditioned Problems Itay Safran, Ohad Shamir
Analytic Study of Families of Spurious Minima in Two-Layer ReLU Neural Networks: A Tale of Symmetry II Yossi Arjevani, Michael Field
CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks Sakshi Varshney, Vinay Kumar Verma, P. K. Srijith, Lawrence Carin, Piyush Rai
Structured Dropout Variational Inference for Bayesian Neural Networks Son Nguyen, Duong Nguyen, Khai Nguyen, Khoat Than, Hung Bui, Nhat Ho
Neural Relightable Participating Media Rendering Quan Zheng, Gurprit Singh, Hans-peter Seidel
Efficient Neural Network Training via Forward and Backward Propagation Sparsification Xiao Zhou, Weizhong Zhang, Zonghao Chen, SHIZHE DIAO, Tong Zhang
Learning to Ground Multi-Agent Communication with Autoencoders Toru Lin, Jacob Huh, Christopher Stauffer, Ser Nam Lim, Phillip Isola
Large-Scale Wasserstein Gradient Flows Petr Mokrov, Alexander Korotin, Lingxiao Li, Aude Genevay, Justin M Solomon, Evgeny Burnaev
Who Leads and Who Follows in Strategic Classification? Tijana Zrnic, Eric Mazumdar, Shankar Sastry, Michael I. Jordan
Unadversarial Examples: Designing Objects for Robust Vision Hadi Salman, Andrew Ilyas, Logan Engstrom, Sai Vemprala, Aleksander Madry, Ashish Kapoor
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings Hengrui Cai, Chengchun Shi, Rui Song, Wenbin Lu
Attention Approximates Sparse Distributed Memory Trenton Bricken, Cengiz Pehlevan
Augmented Shortcuts for Vision Transformers Yehui Tang, Kai Han, Chang Xu, An Xiao, Yiping Deng, Chao Xu, Yunhe Wang
Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance Justin Lim, Christina X Ji, Michael Oberst, Saul Blecker, Leora Horwitz, David Sontag
Identifying and Benchmarking Natural Out-of-Context Prediction Problems David Madras, Richard S. Zemel
Label Disentanglement in Partition-based Extreme Multilabel Classification Xuanqing Liu, Wei-Cheng Chang, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon
Leveraging SE(3) Equivariance for Self-supervised Category-Level Object Pose Estimation from Point Clouds Xiaolong Li, Yijia Weng, Li Yi, Leonidas Guibas, A. Abbott, Shuran Song, He Wang
A Theoretical Analysis of Fine-tuning with Linear Teachers Gal Shachaf, Alon Brutzkus, Amir Globerson
Overinterpretation reveals image classification model pathologies Brandon Carter, Siddhartha Jain, Jonas W Mueller, David Gifford
Neural Circuit Synthesis from Specification Patterns Frederik Schmitt, Christopher Hahn, Markus N Rabe, Bernd Finkbeiner
Directional Message Passing on Molecular Graphs via Synthetic Coordinates Johannes Gasteiger, Chandan Yeshwanth, Stephan Günnemann
Federated Multi-Task Learning under a Mixture of Distributions Othmane Marfoq, Giovanni Neglia, Aurélien Bellet, Laetitia Kameni, Richard Vidal
Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction Jing Zhang, Jianwen Xie, Nick Barnes, Ping Li
Regularization in ResNet with Stochastic Depth Soufiane Hayou, Fadhel Ayed
ResT: An Efficient Transformer for Visual Recognition Qinglong Zhang, Yu-Bin Yang
Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams Chawin Sitawarin, Evgenios Kornaropoulos, Dawn Song, David Wagner
Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions Jiachen Sun, Yulong Cao, Christopher B Choy, Zhiding Yu, Anima Anandkumar, Zhuoqing Morley Mao, Chaowei Xiao
Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL Jack Parker-Holder, Vu Nguyen, Shaan Desai, Stephen J Roberts
Neural Algorithmic Reasoners are Implicit Planners Andreea-Ioana Deac, Petar Veličković, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic
Self-Supervised Learning with Kernel Dependence Maximization Yazhe Li, Roman Pogodin, Danica J. Sutherland, Arthur Gretton
CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age Dani Kiyasseh, Tingting Zhu, David Clifton
Representing Hyperbolic Space Accurately using Multi-Component Floats Tao Yu, Christopher M De Sa
Dimensionality Reduction for Wasserstein Barycenter Zachary Izzo, Sandeep Silwal, Samson Zhou
Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception Joel Dapello, Jenelle Feather, Hang Le, Tiago Marques, David Cox, Josh McDermott, James J DiCarlo, Sueyeon Chung
Unsupervised Learning of Compositional Energy Concepts Yilun Du, Shuang Li, Yash Sharma, Josh Tenenbaum, Igor Mordatch
Nearly Horizon-Free Offline Reinforcement Learning Tongzheng Ren, Jialian Li, Bo Dai, Simon S Du, Sujay Sanghavi
Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approach Ahmed Abbas, Paul Swoboda
Reinforcement Learning with State Observation Costs in Action-Contingent Noiselessly Observable Markov Decision Processes HyunJi Alex Nam, Scott Fleming, Emma Brunskill
Iterative Amortized Policy Optimization Joseph Marino, Alexandre Piche, Alessandro Davide Ialongo, Yisong Yue
Revisiting the Calibration of Modern Neural Networks Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, Mario Lucic
The decomposition of the higher-order homology embedding constructed from the $k$-Laplacian Yu-Chia Chen, Marina Meila
Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs Han Zhong, Jiayi Huang, Lin Yang, Liwei Wang
A nonparametric method for gradual change problems with statistical guarantees Lizhen Nie, Dan Nicolae
Nested Graph Neural Networks Muhan Zhang, Pan Li
Multimodal and Multilingual Embeddings for Large-Scale Speech Mining Paul-Ambroise Duquenne, Hongyu Gong, Holger Schwenk
Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables Jakob Runge
On Blame Attribution for Accountable Multi-Agent Sequential Decision Making Stelios Triantafyllou, Adish Singla, Goran Radanovic
FLEX: Unifying Evaluation for Few-Shot NLP Jonathan Bragg, Arman Cohan, Kyle Lo, Iz Beltagy
A flow-based latent state generative model of neural population responses to natural images Mohammad Bashiri, Edgar Walker, Konstantin-Klemens Lurz, Akshay Jagadish, Taliah Muhammad, Zhiwei Ding, Zhuokun Ding, Andreas Tolias, Fabian H. Sinz
Learnable Fourier Features for Multi-dimensional Spatial Positional Encoding Yang Li, Si Si, Gang Li, Cho-Jui Hsieh, Samy Bengio
Doubly Robust Thompson Sampling with Linear Payoffs Wonyoung Kim, Gi-Soo Kim, Myunghee Cho Paik
A Computationally Efficient Method for Learning Exponential Family Distributions Abhin Shah, Devavrat Shah, Gregory Wornell
Rethinking Neural Operations for Diverse Tasks Nicholas Roberts, Mikhail Khodak, Tri Dao, Liam Li, Christopher Ré, Ameet Talwalkar
Motif-based Graph Self-Supervised Learning for Molecular Property Prediction ZAIXI ZHANG, Qi Liu, Hao Wang, Chengqiang Lu, Chee-Kong Lee
On Inductive Biases for Heterogeneous Treatment Effect Estimation Alicia Curth, Mihaela van der Schaar
DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples Yi Xu, Jiandong Ding, Lu Zhang, Shuigeng Zhou
Transformer in Transformer Kai Han, An Xiao, Enhua Wu, Jianyuan Guo, Chunjing XU, Yunhe Wang
Adversarial Graph Augmentation to Improve Graph Contrastive Learning Susheel Suresh, Pan Li, Cong Hao, Jennifer Neville
Online Control of Unknown Time-Varying Dynamical Systems Edgar Minasyan, Paula Gradu, Max Simchowitz, Elad Hazan
Contrastive Reinforcement Learning of Symbolic Reasoning Domains Gabriel Poesia, WenXin Dong, Noah Goodman
Spatial Ensemble: a Novel Model Smoothing Mechanism for Student-Teacher Framework Tengteng Huang, Yifan Sun, Xun Wang, Haotian Yao, Chi Zhang
Probabilistic Tensor Decomposition of Neural Population Spiking Activity Hugo Soulat, Sepiedeh Keshavarzi, Troy Margrie, Maneesh Sahani
Recurrent Bayesian Classifier Chains for Exact Multi-Label Classification Walter Gerych, Tom Hartvigsen, Luke Buquicchio, Emmanuel Agu, Elke A. Rundensteiner
Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic Yufeng Zhang, Siyu Chen, Zhuoran Yang, Michael I. Jordan, Zhaoran Wang
Assessing Fairness in the Presence of Missing Data Yiliang Zhang, Qi Long
Adversarial Attack Generation Empowered by Min-Max Optimization Jingkang Wang, Tianyun Zhang, Sijia Liu, Pin-Yu Chen, Jiacen Xu, Makan Fardad, Bo Li
Safe Pontryagin Differentiable Programming Wanxin Jin, Shaoshuai Mou, George J. Pappas
Class-Disentanglement and Applications in Adversarial Detection and Defense Kaiwen Yang, Tianyi Zhou, Yonggang Zhang, Xinmei Tian, Dacheng Tao
Active 3D Shape Reconstruction from Vision and Touch Edward Smith, David Meger, Luis Pineda, Roberto Calandra, Jitendra Malik, Adriana Romero Soriano, Michal Drozdzal
CAPE: Encoding Relative Positions with Continuous Augmented Positional Embeddings Tatiana Likhomanenko, Qiantong Xu, Gabriel Synnaeve, Ronan Collobert, Alex Rogozhnikov
Multi-armed Bandit Requiring Monotone Arm Sequences Ningyuan Chen
Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning Xinyi Xu, Lingjuan Lyu, Xingjun Ma, Chenglin Miao, Chuan Sheng Foo, Bryan Kian Hsiang Low
Generalizable Imitation Learning from Observation via Inferring Goal Proximity Youngwoon Lee, Andrew Szot, Shao-Hua Sun, Joseph J. Lim
DualNet: Continual Learning, Fast and Slow Quang Pham, Chenghao Liu, Steven C. Hoi
Deformable Butterfly: A Highly Structured and Sparse Linear Transform Rui Lin, Jie Ran, King Hung Chiu, Graziano Chesi, Ngai Wong
Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning Colin Wei, Sang Michael Xie, Tengyu Ma
Learning Diverse Policies in MOBA Games via Macro-Goals Yiming Gao, Bei Shi, Xueying Du, Liang Wang, Guangwei Chen, Zhenjie Lian, Fuhao Qiu, GUOAN HAN, Weixuan Wang, Deheng Ye, Qiang Fu, Wei Yang, Lanxiao Huang
Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi Ho Chit Siu, Jaime Peña, Edenna Chen, Yutai Zhou, Victor Lopez, Kyle Palko, Kimberlee Chang, Ross Allen
Counterfactual Invariance to Spurious Correlations in Text Classification Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein
Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training Lue Tao, Lei Feng, Jinfeng Yi, Sheng-Jun Huang, Songcan Chen
Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD Rémi Bardenet, Subhroshekhar Ghosh, Meixia LIN
Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Luc V Gool
Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations Hyeong-Seok Choi, Juheon Lee, Wansoo Kim, Jie Lee, Hoon Heo, Kyogu Lee
Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation Fenglin Liu, Chenyu You, Xian Wu, Shen Ge, Sheng wang, Xu Sun
Diffusion Normalizing Flow Qinsheng Zhang, Yongxin Chen
Introspective Distillation for Robust Question Answering Yulei Niu, Hanwang Zhang
Rethinking the Pruning Criteria for Convolutional Neural Network Zhongzhan Huang, Wenqi Shao, Xinjiang Wang, Liang Lin, Ping Luo
Adaptive Machine Unlearning Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites
EditGAN: High-Precision Semantic Image Editing Huan Ling, Karsten Kreis, Daiqing Li, Seung Wook Kim, Antonio Torralba, Sanja Fidler
Deep Molecular Representation Learning via Fusing Physical and Chemical Information Shuwen Yang, Ziyao Li, Guojie Song, Lingsheng Cai
Neural optimal feedback control with local learning rules Johannes Friedrich, Siavash Golkar, Shiva Farashahi, Alexander Genkin, Anirvan Sengupta, Dmitri B. Chklovskii
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta
Noether Networks: meta-learning useful conserved quantities Ferran Alet, Dylan Doblar, Allan Zhou, Josh Tenenbaum, Kenji Kawaguchi, Chelsea Finn
Uncertainty-Driven Loss for Single Image Super-Resolution Qian Ning, Weisheng Dong, Xin Li, Jinjian Wu, GUANGMING Shi
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein
Capacity and Bias of Learned Geometric Embeddings for Directed Graphs Michael Boratko, Dongxu Zhang, Nicholas Monath, Luke Vilnis, Kenneth L Clarkson, Andrew McCallum
Online Learning Of Neural Computations From Sparse Temporal Feedback Lukas Braun, Tim Vogels
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello
Instance-Conditional Knowledge Distillation for Object Detection Zijian Kang, Peizhen Zhang, Xiangyu Zhang, Jian Sun, Nanning Zheng
Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction Konstantin Schürholt, Dimche Kostadinov, Damian Borth
Multimodal Virtual Point 3D Detection Tianwei Yin, Xingyi Zhou, Philipp Krähenbühl
On Joint Learning for Solving Placement and Routing in Chip Design Ruoyu Cheng, Junchi Yan
Learning with Algorithmic Supervision via Continuous Relaxations Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen
Differentiable Multiple Shooting Layers Stefano Massaroli, Michael Poli, Sho Sonoda, Taiji Suzuki, Jinkyoo Park, Atsushi Yamashita, Hajime Asama
Global-aware Beam Search for Neural Abstractive Summarization Ye Ma, Zixun Lan, Lu Zong, Kaizhu Huang
DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras Zachary Teed, Jia Deng
Few-Shot Object Detection via Association and DIscrimination Yuhang Cao, Jiaqi Wang, Ying Jin, Tong Wu, Kai Chen, Ziwei Liu, Dahua Lin
Neural Dubber: Dubbing for Videos According to Scripts Chenxu Hu, Qiao Tian, Tingle Li, Wang Yuping, Yuxuan Wang, Hang Zhao
Neural Bootstrapper Minsuk Shin, Hyungjoo Cho, Hyun-seok Min, Sungbin Lim
An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning Cyrus Cousins
HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning Shiming Chen, Guosen Xie, Yang Liu, Qinmu Peng, Baigui Sun, Hao Li, Xinge You, Ling Shao
Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes Cristopher Salvi, Maud Lemercier, Chong Liu, Blanka Horvath, Theodoros Damoulas, Terry Lyons
Low-Rank Subspaces in GANs Jiapeng Zhu, Ruili Feng, Yujun Shen, Deli Zhao, Zheng-Jun Zha, Jingren Zhou, Qifeng Chen
Neural Symplectic Form: Learning Hamiltonian Equations on General Coordinate Systems Yuhan Chen, Takashi Matsubara, Takaharu Yaguchi
Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting Gen Li, Yuxin Chen, Yuejie Chi, Yuantao Gu, Yuting Wei
Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels Jizong Peng, Ping Wang, Christian Desrosiers, Marco Pedersoli
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems Jimmy Smith, Scott Linderman, David Sussillo
Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds Antonios Antoniadis, Christian Coester, Marek Elias, Adam Polak, Bertrand Simon
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent Priyank Jaini, Lars Holdijk, Max Welling
Information Directed Sampling for Sparse Linear Bandits Botao Hao, Tor Lattimore, Wei Deng
Linear Convergence of Gradient Methods for Estimating Structured Transition Matrices in High-dimensional Vector Autoregressive Models Xiao Lv, Wei Cui, Yulong Liu
Large-Scale Unsupervised Object Discovery Van Huy Vo, Elena Sizikova, Cordelia Schmid, Patrick Pérez, Jean Ponce
Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space Jiehong Lin, Hongyang Li, Ke Chen, Jiangbo Lu, Kui Jia
Noisy Adaptation Generates Lévy Flights in Attractor Neural Networks Xingsi Dong, Tianhao Chu, Tiejun Huang, Zilong Ji, Si Wu
On Linear Stability of SGD and Input-Smoothness of Neural Networks Chao Ma, Lexing Ying
Joint inference and input optimization in equilibrium networks Swaminathan Gurumurthy, Shaojie Bai, Zachary Manchester, J. Zico Kolter
A unified framework for bandit multiple testing Ziyu Xu, Ruodu Wang, Aaditya Ramdas
Recovering Latent Causal Factor for Generalization to Distributional Shifts Xinwei Sun, Botong Wu, Xiangyu Zheng, Chang Liu, Wei Chen, Tao Qin, Tie-Yan Liu
Graph Differentiable Architecture Search with Structure Learning Yijian Qin, Xin Wang, Zeyang Zhang, Wenwu Zhu
Designing Counterfactual Generators using Deep Model Inversion Jayaraman Thiagarajan, Vivek Sivaraman Narayanaswamy, Deepta Rajan, Jia Liang, Akshay Chaudhari, Andreas Spanias
A Faster Maximum Cardinality Matching Algorithm with Applications in Machine Learning Nathaniel Lahn, Sharath Raghvendra, Jiacheng Ye
Dynamic population-based meta-learning for multi-agent communication with natural language Abhinav Gupta, Marc Lanctot, Angeliki Lazaridou
Adversarial Neuron Pruning Purifies Backdoored Deep Models Dongxian Wu, Yisen Wang
Towards Robust and Reliable Algorithmic Recourse Sohini Upadhyay, Shalmali Joshi, Himabindu Lakkaraju
Neural Rule-Execution Tracking Machine For Transformer-Based Text Generation Yufei Wang, Can Xu, Huang Hu, Chongyang Tao, Stephen Wan, Mark Dras, Mark Johnson, Daxin Jiang
Scalable Online Planning via Reinforcement Learning Fine-Tuning Arnaud Fickinger, Hengyuan Hu, Brandon Amos, Stuart J. Russell, Noam Brown
Adversarial Regression with Doubly Non-negative Weighting Matrices Tam Le, Truyen Nguyen, Makoto Yamada, Jose Blanchet, Viet Anh Nguyen
Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection HanQin Cai, Jialin Liu, Wotao Yin
Proxy-Normalizing Activations to Match Batch Normalization while Removing Batch Dependence Antoine Labatie, Dominic Masters, Zach Eaton-Rosen, Carlo Luschi
Dynamic Bottleneck for Robust Self-Supervised Exploration Chenjia Bai, Lingxiao Wang, Lei Han, Animesh Garg, Jianye Hao, Peng Liu, Zhaoran Wang
ProTo: Program-Guided Transformer for Program-Guided Tasks Zelin Zhao, Karan Samel, Binghong Chen, lee song
An Efficient Transfer Learning Framework for Multiagent Reinforcement Learning Tianpei Yang, Weixun Wang, Hongyao Tang, Jianye Hao, Zhaopeng Meng, Hangyu Mao, Dong Li, Wulong Liu, Yingfeng Chen, Yujing Hu, Changjie Fan, Chengwei Zhang
Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneck Nicolas Skatchkovsky, Osvaldo Simeone, Hyeryung Jang
NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform Achille Thin, Yazid Janati El Idrissi, Sylvain Le Corff, Charles Ollion, Eric Moulines, Arnaud Doucet, Alain Durmus, Christian X Robert
Relaxing Local Robustness Klas Leino, Matt Fredrikson
Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer Ge Yang, Edward Hu, Igor Babuschkin, Szymon Sidor, Xiaodong Liu, David Farhi, Nick Ryder, Jakub Pachocki, Weizhu Chen, Jianfeng Gao
Statistical Regeneration Guarantees of the Wasserstein Autoencoder with Latent Space Consistency Anish Chakrabarty, Swagatam Das
Leveraging the Inductive Bias of Large Language Models for Abstract Textual Reasoning Christopher Rytting, David Wingate
Differentiable Simulation of Soft Multi-body Systems Yiling Qiao, Junbang Liang, Vladlen Koltun, Ming Lin
Good Classification Measures and How to Find Them Martijn Gösgens, Anton Zhiyanov, Aleksey Tikhonov, Liudmila Prokhorenkova
Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck Junho Kim, Byung-Kwan Lee, Yong Man Ro
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels Michael Hutchinson, Alexander Terenin, Viacheslav Borovitskiy, So Takao, Yee W. Teh, Marc Deisenroth
On the Representation Power of Set Pooling Networks Christian Bueno, Alan Hylton
Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs Tao Liu, Ruida Zhou, Dileep Kalathil, Panganamala Kumar, Chao Tian
A Prototype-Oriented Framework for Unsupervised Domain Adaptation Korawat Tanwisuth, Xinjie Fan, Huangjie Zheng, Shujian Zhang, Hao Zhang, Bo Chen, Mingyuan Zhou
Mining the Benefits of Two-stage and One-stage HOI Detection Aixi Zhang, Yue Liao, Si Liu, Miao Lu, Yongliang Wang, Chen Gao, XIAOBO LI
Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation Ying Sun, Hengshu Zhu, Chuan Qin, Fuzhen Zhuang, Qing He, Hui Xiong
Risk-averse Heteroscedastic Bayesian Optimization Anastasia Makarova, Ilnura Usmanova, Ilija Bogunovic, Andreas Krause
Invertible DenseNets with Concatenated LipSwish Yura Perugachi-Diaz, Jakub Tomczak, Sandjai Bhulai
Topological Detection of Trojaned Neural Networks Songzhu Zheng, Yikai Zhang, Hubert Wagner, Mayank Goswami, Chao Chen
Provably Strict Generalisation Benefit for Invariance in Kernel Methods Bryn Elesedy
Formalizing the Generalization-Forgetting Trade-off in Continual Learning Krishnan Raghavan, Prasanna Balaprakash
Risk-Aware Transfer in Reinforcement Learning using Successor Features Michael Gimelfarb, Andre Barreto, Scott Sanner, Chi-Guhn Lee
Causal Inference for Event Pairs in Multivariate Point Processes Tian Gao, Dharmashankar Subramanian, Debarun Bhattacharjya, Xiao Shou, Nicholas Mattei, Kristin P Bennett
Evaluating model performance under worst-case subpopulations Mike Li, Hongseok Namkoong, Shangzhou Xia
Privately Publishable Per-instance Privacy Rachel Redberg, Yu-Xiang Wang
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm
Coresets for Clustering with Missing Values Vladimir Braverman, Shaofeng Jiang, Robert Krauthgamer, Xuan Wu
Boosting with Multiple Sources Corinna Cortes, Mehryar Mohri, Dmitry Storcheus, Ananda Theertha Suresh
Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation Bowen Zhang, Yifan liu, Zhi Tian, Chunhua Shen
Dense Keypoints via Multiview Supervision Zhixuan Yu, Haozheng Yu, Long Sha, Sujoy Ganguly, Hyun Soo Park
Scatterbrain: Unifying Sparse and Low-rank Attention Beidi Chen, Tri Dao, Eric Winsor, Zhao Song, Atri Rudra, Christopher Ré
PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning Yining Hong, Li Yi, Josh Tenenbaum, Antonio Torralba, Chuang Gan
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction Yaqing Wang, Abulikemu Abuduweili, Quanming Yao, Dejing Dou
Differentially Private Learning with Adaptive Clipping Galen Andrew, Om Thakkar, Brendan McMahan, Swaroop Ramaswamy
Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial Yang Liu, Jialu Wang
Projected GANs Converge Faster Axel Sauer, Kashyap Chitta, Jens Müller, Andreas Geiger
Generating High-Quality Explanations for Navigation in Partially-Revealed Environments Gregory Stein
De-randomizing MCMC dynamics with the diffusion Stein operator Zheyang Shen, Markus Heinonen, Samuel Kaski
Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains Christian Gumbsch, Martin V. Butz, Georg Martius
PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning Neehar Peri, Michael Curry, Samuel Dooley, John Dickerson
Large-Scale Learning with Fourier Features and Tensor Decompositions Frederiek Wesel, Kim Batselier
Hash Layers For Large Sparse Models Stephen Roller, Sainbayar Sukhbaatar, arthur szlam, Jason Weston
Sliced Mutual Information: A Scalable Measure of Statistical Dependence Ziv Goldfeld, Kristjan Greenewald
Emergent Communication under Varying Sizes and Connectivities Jooyeon Kim, Alice Oh
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks Rong Zhu, Mattia Rigotti
Regret Minimization Experience Replay in Off-Policy Reinforcement Learning Xu-Hui Liu, Zhenghai Xue, Jingcheng Pang, Shengyi Jiang, Feng Xu, Yang Yu
Relative Uncertainty Learning for Facial Expression Recognition Yuhang Zhang, Chengrui Wang, Weihong Deng
An Information-theoretic Approach to Distribution Shifts Marco Federici, Ryota Tomioka, Patrick Forré
TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model Smoothness Zhuolin Yang, Linyi Li, Xiaojun Xu, Shiliang Zuo, Qian Chen, Pan Zhou, Benjamin I. Rubinstein, Ce Zhang, Bo Li
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors Zhaoqiang Liu, Subhroshekhar Ghosh, Jonathan Scarlett
Moser Flow: Divergence-based Generative Modeling on Manifolds Noam Rozen, Aditya Grover, Maximilian Nickel, Yaron Lipman
Structure-Aware Random Fourier Kernel for Graphs Jinyuan Fang, Qiang Zhang, Zaiqiao Meng, Shangsong Liang
Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling Valentin De Bortoli, James Thornton, Jeremy Heng, Arnaud Doucet
Improving Transferability of Representations via Augmentation-Aware Self-Supervision Hankook Lee, Kibok Lee, Kimin Lee, Honglak Lee, Jinwoo Shin
Long-Short Transformer: Efficient Transformers for Language and Vision Chen Zhu, Wei Ping, Chaowei Xiao, Mohammad Shoeybi, Tom Goldstein, Anima Anandkumar, Bryan Catanzaro
Post-Training Sparsity-Aware Quantization Gil Shomron, Freddy Gabbay, Samer Kurzum, Uri Weiser
The Implicit Bias of Minima Stability: A View from Function Space Rotem Mulayoff, Tomer Michaeli, Daniel Soudry
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning Gen Li, Laixi Shi, Yuxin Chen, Yuantao Gu, Yuejie Chi
Robust Auction Design in the Auto-bidding World Santiago Balseiro, Yuan Deng, Jieming Mao, Vahab Mirrokni, Song Zuo
Weighted model estimation for offline model-based reinforcement learning Toru Hishinuma, Kei Senda
Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers Julian Katz-Samuels, Blake Mason, Kevin G. Jamieson, Rob Nowak
Deconditional Downscaling with Gaussian Processes Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic
Image Generation using Continuous Filter Atoms Ze Wang, Seunghyun Hwang, Zichen Miao, Qiang Qiu
Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons Paul Haider, Benjamin Ellenberger, Laura Kriener, Jakob Jordan, Walter Senn, Mihai A. Petrovici
Learning Fast-Inference Bayesian Networks Vaidyanathan Peruvemba Ramaswamy, Stefan Szeider
Per-Pixel Classification is Not All You Need for Semantic Segmentation Bowen Cheng, Alex Schwing, Alexander Kirillov
Deep Markov Factor Analysis: Towards Concurrent Temporal and Spatial Analysis of fMRI Data Amirreza Farnoosh, Sarah Ostadabbas
BooVAE: Boosting Approach for Continual Learning of VAE Evgenii Egorov, Anna Kuzina, Evgeny Burnaev
Handling Long-tailed Feature Distribution in AdderNets Minjing Dong, Yunhe Wang, Xinghao Chen, Chang Xu
Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL Minshuo Chen, Yan Li, Ethan Wang, Zhuoran Yang, Zhaoran Wang, Tuo Zhao
A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning Gugan Chandrashekhar Thoppe, Bhumesh Kumar
MOMA: Multi-Object Multi-Actor Activity Parsing Zelun Luo, Wanze Xie, Siddharth Kapoor, Yiyun Liang, Michael Cooper, Juan Carlos Niebles, Ehsan Adeli, Fei-Fei Li
The Pareto Frontier of model selection for general Contextual Bandits Teodor Vanislavov Marinov, Julian Zimmert
Teaching an Active Learner with Contrastive Examples Chaoqi Wang, Adish Singla, Yuxin Chen
Structured Denoising Diffusion Models in Discrete State-Spaces Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, Rianne van den Berg
Emergent Communication of Generalizations Jesse Mu, Noah Goodman
Distributed Machine Learning with Sparse Heterogeneous Data Dominic Richards, Sahand Negahban, Patrick Rebeschini
Manipulating SGD with Data Ordering Attacks I Shumailov, Zakhar Shumaylov, Dmitry Kazhdan, Yiren Zhao, Nicolas Papernot, Murat A Erdogdu, Ross J Anderson
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann
Locality Sensitive Teaching Zhaozhuo Xu, Beidi Chen, Chaojian Li, Weiyang Liu, Le Song, Yingyan Lin, Anshumali Shrivastava
No-Press Diplomacy from Scratch Anton Bakhtin, David Wu, Adam Lerer, Noam Brown
Remember What You Want to Forget: Algorithms for Machine Unlearning Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh
Learning latent causal graphs via mixture oracles Bohdan Kivva, Goutham Rajendran, Pradeep K. Ravikumar, Bryon Aragam
ErrorCompensatedX: error compensation for variance reduced algorithms Hanlin Tang, Yao Li, Ji Liu, Ming Yan
Deep Contextual Video Compression Jiahao Li, Bin Li, Yan Lu
On the Frequency Bias of Generative Models Katja Schwarz, Yiyi Liao, Andreas Geiger
Learning curves of generic features maps for realistic datasets with a teacher-student model Bruno Loureiro, Cedric Gerbelot, Hugo Cui, Sebastian Goldt, Florent Krzakala, Marc Mezard, Lenka Zdeborová
It Has Potential: Gradient-Driven Denoisers for Convergent Solutions to Inverse Problems Regev Cohen, Yochai Blau, Daniel Freedman, Ehud Rivlin
Training Over-parameterized Models with Non-decomposable Objectives Harikrishna Narasimhan, Aditya K Menon
Reinforcement learning for optimization of variational quantum circuit architectures Mateusz Ostaszewski, Lea M. Trenkwalder, Wojciech Masarczyk, Eleanor Scerri, Vedran Dunjko
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices Max Ryabinin, Eduard Gorbunov, Vsevolod Plokhotnyuk, Gennady Pekhimenko
IRM—when it works and when it doesn't: A test case of natural language inference Yana Dranker, He He, Yonatan Belinkov
Self-Supervised Learning Disentangled Group Representation as Feature Tan Wang, Zhongqi Yue, Jianqiang Huang, Qianru Sun, Hanwang Zhang
SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning Aaron Chan, Jiashu Xu, Boyuan Long, Soumya Sanyal, Tanishq Gupta, Xiang Ren
Supervising the Transfer of Reasoning Patterns in VQA Corentin Kervadec, Christian Wolf, Grigory Antipov, Moez Baccouche, Madiha Nadri
Conformal Bayesian Computation Edwin Fong, Chris C Holmes
A Unified Approach to Fair Online Learning via Blackwell Approachability Evgenii Chzhen, Christophe Giraud, Gilles Stoltz
Training Neural Networks is ER-complete Mikkel Abrahamsen, Linda Kleist, Tillmann Miltzow
Understanding the Under-Coverage Bias in Uncertainty Estimation Yu Bai, Song Mei, Huan Wang, Caiming Xiong
Decentralized Q-learning in Zero-sum Markov Games Muhammed Sayin, Kaiqing Zhang, David Leslie, Tamer Basar, Asuman Ozdaglar
Fast Certified Robust Training with Short Warmup Zhouxing Shi, Yihan Wang, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh
Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices Federico López, Beatrice Pozzetti, Steve Trettel, Michael Strube, Anna Wienhard
Improved Transformer for High-Resolution GANs Long Zhao, Zizhao Zhang, Ting Chen, Dimitris Metaxas, Han Zhang
Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence Xue Yang, Xiaojiang Yang, Jirui Yang, Qi Ming, Wentao Wang, Qi Tian, Junchi Yan
On Locality of Local Explanation Models Sahra Ghalebikesabi, Lucile Ter-Minassian, Karla DiazOrdaz, Chris C Holmes
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling Bowen Zhang, Yidong Wang, Wenxin Hou, HAO WU, Jindong Wang, Manabu Okumura, Takahiro Shinozaki
Relative Flatness and Generalization Henning Petzka, Michael Kamp, Linara Adilova, Cristian Sminchisescu, Mario Boley
The Image Local Autoregressive Transformer Chenjie Cao, Yuxin Hong, Xiang Li, Chengrong Wang, Chengming Xu, Yanwei Fu, Xiangyang Xue
Towards Multi-Grained Explainability for Graph Neural Networks Xiang Wang, Yingxin Wu, An Zhang, Xiangnan He, Tat-Seng Chua
Behavior From the Void: Unsupervised Active Pre-Training Hao Liu, Pieter Abbeel
Autonomous Reinforcement Learning via Subgoal Curricula Archit Sharma, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn
Statistically and Computationally Efficient Linear Meta-representation Learning Kiran K Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
Decentralized Learning in Online Queuing Systems Flore Sentenac, Etienne Boursier, Vianney Perchet
Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning Yizhen Zhang, Minkyu Choi, Kuan Han, Zhongming Liu
BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining Weizhe Hua, Yichi Zhang, Chuan Guo, Zhiru Zhang, G. Edward Suh
Neural Distance Embeddings for Biological Sequences Gabriele Corso, Zhitao Ying, Michal Pándy, Petar Veličković, Jure Leskovec, Pietro Liò
Fitting summary statistics of neural data with a differentiable spiking network simulator Guillaume Bellec, Shuqi Wang, Alireza Modirshanechi, Johanni Brea, Wulfram Gerstner
PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators Anish Agarwal, Abdullah Alomar, Varkey Alumootil, Devavrat Shah, Dennis Shen, Zhi Xu, Cindy Yang
All Tokens Matter: Token Labeling for Training Better Vision Transformers Zi-Hang Jiang, Qibin Hou, Li Yuan, Daquan Zhou, Yujun Shi, Xiaojie Jin, Anran Wang, Jiashi Feng
Partition and Code: learning how to compress graphs Giorgos Bouritsas, Andreas Loukas, Nikolaos Karalias, Michael Bronstein
Knowledge-inspired 3D Scene Graph Prediction in Point Cloud Shoulong Zhang, shuai li, Aimin Hao, Hong Qin
Online Variational Filtering and Parameter Learning Andrew Campbell, Yuyang Shi, Thomas Rainforth, Arnaud Doucet
Heavy Ball Neural Ordinary Differential Equations Hedi Xia, Vai Suliafu, Hangjie Ji, Tan Nguyen, Andrea Bertozzi, Stanley Osher, Bao Wang
Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families Goutham Rajendran, Bohdan Kivva, Ming Gao, Bryon Aragam
On the Sample Complexity of Learning under Geometric Stability Alberto Bietti, Luca Venturi, Joan Bruna
SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios Suraj Kothawade, Nathan Beck, Krishnateja Killamsetty, Rishabh Iyer
Monte Carlo Tree Search With Iteratively Refining State Abstractions Samuel Sokota, Caleb Y Ho, Zaheen Ahmad, J. Zico Kolter
Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning Danruo DENG, Guangyong Chen, Jianye Hao, Qiong Wang, Pheng-Ann Heng
Taxonomizing local versus global structure in neural network loss landscapes Yaoqing Yang, Liam Hodgkinson, Ryan Theisen, Joe Zou, Joseph E Gonzalez, Kannan Ramchandran, Michael W. Mahoney
Learning Models for Actionable Recourse Alexis Ross, Himabindu Lakkaraju, Osbert Bastani
Efficient and Accurate Gradients for Neural SDEs Patrick Kidger, James Foster, Xuechen (Chen) Li, Terry Lyons
EIGNN: Efficient Infinite-Depth Graph Neural Networks Juncheng Liu, Kenji Kawaguchi, Bryan Hooi, Yiwei Wang, Xiaokui Xiao
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms Alexander Camuto, George Deligiannidis, Murat A Erdogdu, Mert Gurbuzbalaban, Umut Simsekli, Lingjiong Zhu
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Bandit Phase Retrieval Tor Lattimore, Botao Hao
Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions Yin Tat Lee, Ruoqi Shen, Kevin Tian
Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning Xin Zhang, Zhuqing Liu, Jia Liu, Zhengyuan Zhu, Songtao Lu
Federated Graph Classification over Non-IID Graphs Han Xie, Jing Ma, Li Xiong, Carl Yang
SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning Talip Ucar, Ehsan Hajiramezanali, Lindsay Edwards
Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance Hongjian Wang, Mert Gurbuzbalaban, Lingjiong Zhu, Umut Simsekli, Murat A Erdogdu
Conflict-Averse Gradient Descent for Multi-task learning Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, Qiang Liu
Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate Xingyuan Sun, Tianju Xue, Szymon Rusinkiewicz, Ryan P. Adams
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination Dylan J Foster, Akshay Krishnamurthy
Distributed Estimation with Multiple Samples per User: Sharp Rates and Phase Transition Jayadev Acharya, Clement Canonne, Yuhan Liu, Ziteng Sun, Himanshu Tyagi
Revisiting Deep Learning Models for Tabular Data Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, Artem Babenko
Backdoor Attack with Imperceptible Input and Latent Modification Khoa Doan, Yingjie Lao, Ping Li
SOPE: Spectrum of Off-Policy Estimators Christina Yuan, Yash Chandak, Stephen Giguere, Philip S. Thomas, Scott Niekum
Label-Imbalanced and Group-Sensitive Classification under Overparameterization Ganesh Ramachandra Kini, Orestis Paraskevas, Samet Oymak, Christos Thrampoulidis
Neural Program Generation Modulo Static Analysis Rohan Mukherjee, Yeming Wen, Dipak Chaudhari, Thomas Reps, Swarat Chaudhuri, Christopher Jermaine
Unfolding Taylor's Approximations for Image Restoration man zhou, Xueyang Fu, Zeyu Xiao, Gang Yang, Aiping Liu, Zhiwei Xiong
Metropolis-Hastings Data Augmentation for Graph Neural Networks Hyeonjin Park, Seunghun Lee, Sihyeon Kim, Jinyoung Park, Jisu Jeong, Kyung-Min Kim, Jung-Woo Ha, Hyunwoo J Kim
Strategic Behavior is Bliss: Iterative Voting Improves Social Welfare Joshua Kavner, Lirong Xia
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations Ayush Sekhari, Christoph Dann, Mehryar Mohri, Yishay Mansour, Karthik Sridharan
Functional Regularization for Reinforcement Learning via Learned Fourier Features Alexander Li, Deepak Pathak
Adaptive First-Order Methods Revisited: Convex Minimization without Lipschitz Requirements Kimon Antonakopoulos, Panayotis Mertikopoulos
Adapting to function difficulty and growth conditions in private optimization Hilal Asi, Daniel Levy, John C. Duchi
Support Recovery of Sparse Signals from a Mixture of Linear Measurements Soumyabrata Pal, Arya Mazumdar, Venkata Gandikota
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity Nicolas Loizou, Hugo Berard, Gauthier Gidel, Ioannis Mitliagkas, Simon Lacoste-Julien
Tighter Expected Generalization Error Bounds via Wasserstein Distance Borja Rodríguez Gálvez, German Bassi, Ragnar Thobaben, Mikael Skoglund
Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization Deeksha Adil, Brian Bullins, Sushant Sachdeva
Bridging the Imitation Gap by Adaptive Insubordination Luca Weihs, Unnat Jain, Iou-Jen Liu, Jordi Salvador, Svetlana Lazebnik, Aniruddha Kembhavi, Alex Schwing
Adversarial Robustness with Non-uniform Perturbations Ecenaz Erdemir, Jeffrey Bickford, Luca Melis, Sergul Aydore
Container: Context Aggregation Networks peng gao, Jiasen Lu, hongsheng Li, Roozbeh Mottaghi, Aniruddha Kembhavi
ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs Zhanqiu Zhang, Jie Wang, Jiajun Chen, Shuiwang Ji, Feng Wu
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina F Balcan, Virginia Smith, Ameet Talwalkar
Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Time Anshul Nasery, Soumyadeep Thakur, Vihari Piratla, Abir De, Sunita Sarawagi
Agent Modelling under Partial Observability for Deep Reinforcement Learning Georgios Papoudakis, Filippos Christianos, Stefano Albrecht
Leveraging Distribution Alignment via Stein Path for Cross-Domain Cold-Start Recommendation Weiming Liu, Jiajie Su, Chaochao Chen, Xiaolin Zheng
Conservative Offline Distributional Reinforcement Learning Yecheng Ma, Dinesh Jayaraman, Osbert Bastani
Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics Carles Domingo i Enrich, Youssef Mroueh
Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning Aurelien Bibaut, Nathan Kallus, Maria Dimakopoulou, Antoine Chambaz, Mark van der Laan
Bayesian Optimization with High-Dimensional Outputs Wesley J Maddox, Maximilian Balandat, Andrew G Wilson, Eytan Bakshy
Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks Chen Ma, Xiangyu Guo, Li Chen, Jun-Hai Yong, Yisen Wang
Scalable Diverse Model Selection for Accessible Transfer Learning Daniel Bolya, Rohit Mittapalli, Judy Hoffman
Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering Vincent Sitzmann, Semon Rezchikov, Bill Freeman, Josh Tenenbaum, Fredo Durand
ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction Gengshan Yang, Deqing Sun, Varun Jampani, Daniel Vlasic, Forrester Cole, Ce Liu, Deva Ramanan
Understanding the Effect of Stochasticity in Policy Optimization Jincheng Mei, Bo Dai, Chenjun Xiao, Csaba Szepesvari, Dale Schuurmans
Fine-Grained Zero-Shot Learning with DNA as Side Information Sarkhan Badirli, Zeynep Akata, George Mohler, Christine Picard, Mehmet M Dundar
Optimal Underdamped Langevin MCMC Method Zhengmian Hu, Feihu Huang, Heng Huang
Scheduling jobs with stochastic holding costs Dabeen Lee, Milan Vojnovic
REMIPS: Physically Consistent 3D Reconstruction of Multiple Interacting People under Weak Supervision Mihai Fieraru, Mihai Zanfir, Teodor Szente, Eduard Bazavan, Vlad Olaru, Cristian Sminchisescu
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise Guodong Zhang, Kyle Hsu, Jianing Li, Chelsea Finn, Roger B Grosse
PSD Representations for Effective Probability Models Alessandro Rudi, Carlo Ciliberto
Exploiting a Zoo of Checkpoints for Unseen Tasks Jiaji Huang, Qiang Qiu, Kenneth W. Church
Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach Qitian Wu, Chenxiao Yang, Junchi Yan
Adversarial Teacher-Student Representation Learning for Domain Generalization Fu-En Yang, Yuan-Chia Cheng, Zu-Yun Shiau, Yu-Chiang Frank Wang
Stochastic bandits with groups of similar arms. Fabien Pesquerel, Hassan SABER, Odalric-Ambrym Maillard
Tracking Without Re-recognition in Humans and Machines Drew Linsley, Girik Malik, Junkyung Kim, Lakshmi Narasimhan Govindarajan, Ennio Mingolla, Thomas Serre
Rethinking conditional GAN training: An approach using geometrically structured latent manifolds Sameera Ramasinghe, Moshiur Farazi, Salman H Khan, Nick Barnes, Stephen Gould
How to transfer algorithmic reasoning knowledge to learn new algorithms? Louis-Pascal Xhonneux, Andreea-Ioana Deac, Petar Veličković, Jian Tang
Fast Axiomatic Attribution for Neural Networks Robin Hesse, Simone Schaub-Meyer, Stefan Roth
OSOA: One-Shot Online Adaptation of Deep Generative Models for Lossless Compression Chen Zhang, Shifeng Zhang, Fabio Maria Carlucci, Zhenguo Li
Compressive Visual Representations Kuang-Huei Lee, Anurag Arnab, Sergio Guadarrama, John Canny, Ian Fischer
Multi-Armed Bandits with Bounded Arm-Memory: Near-Optimal Guarantees for Best-Arm Identification and Regret Minimization Arnab Maiti, Vishakha Patil, Arindam Khan
Grounding inductive biases in natural images: invariance stems from variations in data Diane Bouchacourt, Mark Ibrahim, Ari Morcos
Directed Graph Contrastive Learning Zekun Tong, Yuxuan Liang, Henghui Ding, Yongxing Dai, Xinke Li, Changhu Wang
Space-time Mixing Attention for Video Transformer Adrian Bulat, Juan Manuel Perez Rua, Swathikiran Sudhakaran, Brais Martinez, Georgios Tzimiropoulos
Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis Atsushi Nitanda, Denny Wu, Taiji Suzuki
Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning Guiliang Liu, Xiangyu Sun, Oliver Schulte, Pascal Poupart
Only Train Once: A One-Shot Neural Network Training And Pruning Framework Tianyi Chen, Bo Ji, Tianyu Ding, Biyi Fang, Guanyi Wang, Zhihui Zhu, Luming Liang, Yixin Shi, Sheng Yi, Xiao Tu
Referring Transformer: A One-step Approach to Multi-task Visual Grounding Muchen Li, Leonid Sigal
Decoupling the Depth and Scope of Graph Neural Networks Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, Ren Chen
Fast and Memory Efficient Differentially Private-SGD via JL Projections Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Hanwen Shen, Uthaipon Tantipongpipat
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu, Pin-Yu Chen
Pipeline Combinators for Gradual AutoML Guillaume Baudart, Martin Hirzel, Kiran Kate, Parikshit Ram, Avi Shinnar, Jason Tsay
Boost Neural Networks by Checkpoints Feng Wang, Guoyizhe Wei, Qiao Liu, Jinxiang Ou, xian wei, Hairong Lv
Model Selection for Bayesian Autoencoders Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla, Maurizio Filippone
Three Operator Splitting with Subgradients, Stochastic Gradients, and Adaptive Learning Rates Alp Yurtsever, Alex Gu, Suvrit Sra
Knowledge-Adaptation Priors Mohammad Emtiyaz Khan, Siddharth Swaroop
Provably efficient multi-task reinforcement learning with model transfer Chicheng Zhang, Zhi Wang
Predicting Molecular Conformation via Dynamic Graph Score Matching Shitong Luo, Chence Shi, Minkai Xu, Jian Tang
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodriguez, Chao Zhang, B. Aditya Prakash
Bounds all around: training energy-based models with bidirectional bounds Cong Geng, Jia Wang, Zhiyong Gao, Jes Frellsen, Søren Hauberg
CogView: Mastering Text-to-Image Generation via Transformers Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, Jie Tang
Time-independent Generalization Bounds for SGLD in Non-convex Settings Tyler Farghly, Patrick Rebeschini
Nonuniform Negative Sampling and Log Odds Correction with Rare Events Data HaiYing Wang, Aonan Zhang, Chong Wang
Algorithmic stability and generalization of an unsupervised feature selection algorithm xinxing wu, Qiang Cheng
On learning sparse vectors from mixture of responses Nikita Polyanskii
Convergence and Alignment of Gradient Descent with Random Backpropagation Weights Ganlin Song, Ruitu Xu, John D. Lafferty
Adder Attention for Vision Transformer Han Shu, Jiahao Wang, Hanting Chen, Lin Li, Yujiu Yang, Yunhe Wang
Reverse engineering learned optimizers reveals known and novel mechanisms Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, Jascha Sohl-Dickstein
Matching a Desired Causal State via Shift Interventions Jiaqi Zhang, Chandler Squires, Caroline Uhler
Unsupervised Noise Adaptive Speech Enhancement by Discriminator-Constrained Optimal Transport Hsin-Yi Lin, Huan-Hsin Tseng, Xugang Lu, Yu Tsao
Optimality of variational inference for stochasticblock model with missing links Solenne Gaucher, Olga Klopp
Policy Learning Using Weak Supervision Jingkang Wang, Hongyi Guo, Zhaowei Zhu, Yang Liu
Chasing Sparsity in Vision Transformers: An End-to-End Exploration Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang
Graphical Models in Heavy-Tailed Markets Jose Vinicius de Miranda Cardoso, Jiaxi Ying, Daniel Palomar
A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis Xingang Pan, Xudong XU, Chen Change Loy, Christian Theobalt, Bo Dai
XCiT: Cross-Covariance Image Transformers Alaaeldin Ali, Hugo Touvron, Mathilde Caron, Piotr Bojanowski, Matthijs Douze, Armand Joulin, Ivan Laptev, Natalia Neverova, Gabriel Synnaeve, Jakob J. Verbeek, Herve Jegou
Row-clustering of a Point Process-valued Matrix Lihao Yin, Ganggang Xu, Huiyan Sang, Yongtao Guan
Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information Yang Zhang, Ashkan Khakzar, Yawei Li, Azade Farshad, Seong Tae Kim, Nassir Navab
Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints Maura Pintor, Fabio Roli, Wieland Brendel, Battista Biggio
Uncertainty Quantification and Deep Ensembles Rahul Rahaman, alexandre thiery
Directed Probabilistic Watershed Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht
Laplace Redux - Effortless Bayesian Deep Learning Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig
Hessian Eigenspectra of More Realistic Nonlinear Models Zhenyu Liao, Michael W. Mahoney
Explicable Reward Design for Reinforcement Learning Agents Rati Devidze, Goran Radanovic, Parameswaran Kamalaruban, Adish Singla
A Minimalist Approach to Offline Reinforcement Learning Scott Fujimoto, Shixiang (Shane) Gu
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition Rishabh Kabra, Daniel Zoran, Goker Erdogan, Loic Matthey, Antonia Creswell, Matt Botvinick, Alexander Lerchner, Chris Burgess
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning ZHENHUAN YANG, Yunwen Lei, Puyu Wang, Tianbao Yang, Yiming Ying
User-Level Differentially Private Learning via Correlated Sampling Badih Ghazi, Ravi Kumar, Pasin Manurangsi
Asynchronous Decentralized Online Learning Jiyan Jiang, Wenpeng Zhang, Jinjie GU, Wenwu Zhu
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs Raul Astudillo, Daniel Jiang, Maximilian Balandat, Eytan Bakshy, Peter Frazier
Model-Based Domain Generalization Alexander Robey, George J. Pappas, Hamed Hassani
$\alpha$-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression JIABO HE, Sarah Erfani, Xingjun Ma, James Bailey, Ying Chi, Xian-Sheng Hua
Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient David Applegate, Mateo Diaz, Oliver Hinder, Haihao Lu, Miles Lubin, Brendan O'Donoghue, Warren Schudy
On the Provable Generalization of Recurrent Neural Networks Lifu Wang, Bo Shen, Bo Hu, Xing Cao
Differentiable Spline Approximations Minsu Cho, Aditya Balu, Ameya Joshi, Anjana Deva Prasad, Biswajit Khara, Soumik Sarkar, Baskar Ganapathysubramanian, Adarsh Krishnamurthy, Chinmay Hegde
Rate-Optimal Subspace Estimation on Random Graphs Zhixin Zhou, Fan Zhou, Ping Li, Cun-Hui Zhang
Estimating the Unique Information of Continuous Variables Ari Pakman, Amin Nejatbakhsh, Dar Gilboa, Abdullah Makkeh, Luca Mazzucato, Michael Wibral, Elad Schneidman
Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions Ignavier Ng, Yujia Zheng, Jiji Zhang, Kun Zhang
Node Dependent Local Smoothing for Scalable Graph Learning Wentao Zhang, Mingyu Yang, Zeang Sheng, Yang Li, Wen Ouyang, Yangyu Tao, Zhi Yang, Bin CUI
Parallel and Efficient Hierarchical k-Median Clustering Vincent Cohen-Addad, Silvio Lattanzi, Ashkan Norouzi-Fard, Christian Sohler, Ola Svensson
Human-Adversarial Visual Question Answering Sasha Sheng, Amanpreet Singh, Vedanuj Goswami, Jose Magana, Tristan Thrush, Wojciech Galuba, Devi Parikh, Douwe Kiela
Across-animal odor decoding by probabilistic manifold alignment Pedro Herrero-Vidal, Dmitry Rinberg, Cristina Savin
Excess Capacity and Backdoor Poisoning Naren Manoj, Avrim Blum
A Convergence Analysis of Gradient Descent on Graph Neural Networks Pranjal Awasthi, Abhimanyu Das, Sreenivas Gollapudi
Differentiable rendering with perturbed optimizers Quentin Le Lidec, Ivan Laptev, Cordelia Schmid, Justin Carpentier
BCORLE($\lambda$): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce Market Yang Zhang, Bo Tang, Qingyu Yang, Dou An, Hongyin Tang, Chenyang Xi, Xueying LI, Feiyu Xiong
Nested Variational Inference Heiko Zimmermann, Hao Wu, Babak Esmaeili, Jan-Willem van de Meent
Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning Yingjie Fei, Zhuoran Yang, Yudong Chen, Zhaoran Wang
On sensitivity of meta-learning to support data Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun
On Large-Cohort Training for Federated Learning Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, Virginia Smith
Generic Neural Architecture Search via Regression Yuhong Li, Cong Hao, Pan Li, Jinjun Xiong, Deming Chen
The best of both worlds: stochastic and adversarial episodic MDPs with unknown transition Tiancheng Jin, Longbo Huang, Haipeng Luo
Private learning implies quantum stability Yihui Quek, Srinivasan Arunachalam, John A Smolin
Interesting Object, Curious Agent: Learning Task-Agnostic Exploration Simone Parisi, Victoria Dean, Deepak Pathak, Abhinav Gupta
SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity Measurement Heyang Qin, Samyam Rajbhandari, Olatunji Ruwase, Feng Yan, Lei Yang, Yuxiong He
Variational Inference for Continuous-Time Switching Dynamical Systems Lukas Köhs, Bastian Alt, Heinz Koeppl
Implicit Regularization in Matrix Sensing via Mirror Descent Fan Wu, Patrick Rebeschini
STORM+: Fully Adaptive SGD with Recursive Momentum for Nonconvex Optimization Kfir Levy, Ali Kavis, Volkan Cevher
Skipping the Frame-Level: Event-Based Piano Transcription With Neural Semi-CRFs Yujia Yan, Frank Cwitkowitz, Zhiyao Duan
Deep Learning on a Data Diet: Finding Important Examples Early in Training Mansheej Paul, Surya Ganguli, Gintare Karolina Dziugaite
BNS: Building Network Structures Dynamically for Continual Learning Qi Qin, Wenpeng Hu, Han Peng, Dongyan Zhao, Bing Liu
Auditing Black-Box Prediction Models for Data Minimization Compliance Bashir Rastegarpanah, Krishna Gummadi, Mark Crovella
Dueling Bandits with Team Comparisons Lee Cohen, Ulrike Schmidt-Kraepelin, Yishay Mansour
Meta Internal Learning Raphael Bensadoun, Shir Gur, Tomer Galanti, Lior Wolf
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro
Adaptive wavelet distillation from neural networks through interpretations Wooseok Ha, Chandan Singh, Francois Lanusse, Srigokul Upadhyayula, Bin Yu
Generative Occupancy Fields for 3D Surface-Aware Image Synthesis Xudong XU, Xingang Pan, Dahua Lin, Bo Dai
Relaxed Marginal Consistency for Differentially Private Query Answering Ryan McKenna, Siddhant Pradhan, Daniel R. Sheldon, Gerome Miklau
Local policy search with Bayesian optimization Sarah Müller, Alexander von Rohr, Sebastian Trimpe
DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks Wei Sun, Aojun Zhou, Sander Stuijk, Rob Wijnhoven, Andrew Oakleigh Nelson, hongsheng Li, Henk Corporaal
Techniques for Symbol Grounding with SATNet Sever Topan, David Rolnick, Xujie Si
Object DGCNN: 3D Object Detection using Dynamic Graphs Yue Wang, Justin M Solomon
Safe Policy Optimization with Local Generalized Linear Function Approximations Akifumi Wachi, Yunyue Wei, Yanan Sui
Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory Takashi Matsubara, Yuto Miyatake, Takaharu Yaguchi
Exponential Separation between Two Learning Models and Adversarial Robustness Grzegorz Gluch, Ruediger Urbanke
The balancing principle for parameter choice in distance-regularized domain adaptation Werner Zellinger, Natalia Shepeleva, Marius-Constantin Dinu, Hamid Eghbal-zadeh, Hoan Duc Nguyen, Bernhard Nessler, Sergei Pereverzyev, Bernhard A. Moser
Gaussian Kernel Mixture Network for Single Image Defocus Deblurring Yuhui Quan, Zicong Wu, Hui Ji
Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks Frank Schneider, Felix Dangel, Philipp Hennig
MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge Geng Yuan, Xiaolong Ma, Wei Niu, Zhengang Li, Zhenglun Kong, Ning Liu, Yifan Gong, Zheng Zhan, Chaoyang He, Qing Jin, Siyue Wang, Minghai Qin, Bin Ren, Yanzhi Wang, Sijia Liu, Xue Lin
Precise characterization of the prior predictive distribution of deep ReLU networks Lorenzo Noci, Gregor Bachmann, Kevin Roth, Sebastian Nowozin, Thomas Hofmann
RED : Looking for Redundancies for Data-FreeStructured Compression of Deep Neural Networks Edouard YVINEC, Arnaud Dapogny, Matthieu Cord, Kevin Bailly
TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks YU LI, Min LI, Qiuxia LAI, Yannan Liu, Qiang Xu
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods Derek Lim, Felix Hohne, Xiuyu Li, Sijia Linda Huang, Vaishnavi Gupta, Omkar Bhalerao, Ser Nam Lim
Reinforcement Learning based Disease Progression Model for Alzheimer’s Disease Krishnakant Saboo, Anirudh Choudhary, Yurui Cao, Gregory Worrell, David Jones, Ravishankar Iyer
Catch-A-Waveform: Learning to Generate Audio from a Single Short Example Gal Greshler, Tamar Shaham, Tomer Michaeli
Explanation-based Data Augmentation for Image Classification Sandareka Wickramanayake, Wynne Hsu, Mong Li Lee
Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective Tianlong Chen, Yu Cheng, Zhe Gan, Jingjing Liu, Zhangyang Wang
When Are Solutions Connected in Deep Networks? Quynh N Nguyen, Pierre Bréchet, Marco Mondelli
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William Cheung, James T. Kwok
Learning Graph Cellular Automata Daniele Grattarola, Lorenzo Livi, Cesare Alippi
Efficient Online Estimation of Causal Effects by Deciding What to Observe Shantanu Gupta, Zachary Lipton, David Childers
Perturbation Theory for the Information Bottleneck Vudtiwat Ngampruetikorn, David J Schwab
Deconvolutional Networks on Graph Data Jia Li, Jiajin Li, Yang Liu, Jianwei Yu, Yueting Li, Hong Cheng
Variational Multi-Task Learning with Gumbel-Softmax Priors Jiayi Shen, Xiantong Zhen, Marcel Worring, Ling Shao
Accelerating Quadratic Optimization with Reinforcement Learning Jeffrey Ichnowski, Paras Jain, Bartolomeo Stellato, Goran Banjac, Michael Luo, Francesco Borrelli, Joseph E Gonzalez, Ion Stoica, Ken Goldberg
Deep Residual Learning in Spiking Neural Networks Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothée Masquelier, Yonghong Tian
Duplex Sequence-to-Sequence Learning for Reversible Machine Translation Zaixiang Zheng, Hao Zhou, Shujian Huang, Jiajun Chen, Jingjing Xu, Lei Li
Improved Coresets and Sublinear Algorithms for Power Means in Euclidean Spaces Vincent Cohen-Addad, David Saulpic, Chris Schwiegelshohn
Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N:M Transposable Masks Itay Hubara, Brian Chmiel, Moshe Island, Ron Banner, Joseph Naor, Daniel Soudry
Learning and Generalization in RNNs Abhishek Panigrahi, Navin Goyal
Improving Visual Quality of Image Synthesis by A Token-based Generator with Transformers Yanhong Zeng, Huan Yang, Hongyang Chao, Jianbo Wang, Jianlong Fu
The Effect of the Intrinsic Dimension on the Generalization of Quadratic Classifiers Fabian Latorre, Leello Tadesse Dadi, Paul Rolland, Volkan Cevher
DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning Hang Xu, Kelly Kostopoulou, Aritra Dutta, Xin Li, Alexandros Ntoulas, Panos Kalnis
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data Lingxiao Wang, Zhuoran Yang, Zhaoran Wang
Predicting Deep Neural Network Generalization with Perturbation Response Curves Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen
Exploiting Domain-Specific Features to Enhance Domain Generalization Manh-Ha Bui, Toan Tran, Anh Tran, Dinh Phung
Optimal Order Simple Regret for Gaussian Process Bandits Sattar Vakili, Nacime Bouziani, Sepehr Jalali, Alberto Bernacchia, Da-shan Shiu
Generalization Guarantee of SGD for Pairwise Learning Yunwen Lei, Mingrui Liu, Yiming Ying
Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer Junya Chen, Zidi Xiu, Benjamin Goldstein, Ricardo Henao, Lawrence Carin, Chenyang Tao
Heavy Ball Momentum for Conditional Gradient Bingcong Li, Alireza Sadeghi, Georgios Giannakis
PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition Cheng-I Jeff Lai, Yang Zhang, Alexander H. Liu, Shiyu Chang, Yi-Lun Liao, Yung-Sung Chuang, Kaizhi Qian, Sameer Khurana, David Cox, Jim Glass
Robust Learning of Optimal Auctions Wenshuo Guo, Michael I. Jordan, Emmanouil Zampetakis
Disrupting Deep Uncertainty Estimation Without Harming Accuracy Ido Galil, Ran El-Yaniv
SOFT: Softmax-free Transformer with Linear Complexity Jiachen Lu, Jinghan Yao, Junge Zhang, Xiatian Zhu, Hang Xu, Weiguo Gao, Chunjing XU, Tao Xiang, Li Zhang
Task-Adaptive Neural Network Search with Meta-Contrastive Learning Wonyong Jeong, Hayeon Lee, Geon Park, Eunyoung Hyung, Jinheon Baek, Sung Ju Hwang
Neural Flows: Efficient Alternative to Neural ODEs Marin Biloš, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, Stephan Günnemann
Multi-Objective Meta Learning Feiyang YE, Baijiong Lin, Zhixiong Yue, Pengxin Guo, Qiao Xiao, Yu Zhang
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs Gadi Naveh, Zohar Ringel
Mini-Batch Consistent Slot Set Encoder for Scalable Set Encoding Andreis Bruno, Jeffrey Willette, Juho Lee, Sung Ju Hwang
Efficient and Local Parallel Random Walks Michael Kapralov, Silvio Lattanzi, Navid Nouri, Jakab Tardos
Amortized Variational Inference for Simple Hierarchical Models Abhinav Agrawal, Justin Domke
Online Matching in Sparse Random Graphs: Non-Asymptotic Performances of Greedy Algorithm Nathan Noiry, Vianney Perchet, Flore Sentenac
End-to-end reconstruction meets data-driven regularization for inverse problems Subhadip Mukherjee, Marcello Carioni, Ozan Öktem, Carola-Bibiane Schönlieb
An online passive-aggressive algorithm for difference-of-squares classification Lawrence K. Saul
Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai, Karthikeyan Shanmugam
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs Runzhong Wang, Zhigang Hua, Gan Liu, Jiayi Zhang, Junchi Yan, Feng Qi, Shuang Yang, Jun Zhou, Xiaokang Yang
Improved Learning Rates of a Functional Lasso-type SVM with Sparse Multi-Kernel Representation shaogao lv, Junhui Wang, Jiankun Liu, Yong Liu
When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning? Lijie Fan, Sijia Liu, Pin-Yu Chen, Gaoyuan Zhang, Chuang Gan
Learning Transferable Features for Point Cloud Detection via 3D Contrastive Co-training Zeng Yihan, Chunwei Wang, Yunbo Wang, Hang Xu, Chaoqiang Ye, Zhen Yang, Chao Ma
SILG: The Multi-domain Symbolic Interactive Language Grounding Benchmark Victor Zhong, Austin W. Hanjie, Sida Wang, Karthik Narasimhan, Luke Zettlemoyer
A Surrogate Objective Framework for Prediction+Programming with Soft Constraints Kai Yan, Jie Yan, Chuan Luo, Liting Chen, Qingwei Lin, Dongmei Zhang
Learning to Predict Trustworthiness with Steep Slope Loss Yan Luo, Yongkang Wong, Mohan S Kankanhalli, Qi Zhao
On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay Ekaterina Lobacheva, Maxim Kodryan, Nadezhda Chirkova, Andrey Malinin, Dmitry P Vetrov
NeRV: Neural Representations for Videos Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser Nam Lim, Abhinav Shrivastava
Surrogate Regret Bounds for Polyhedral Losses Rafael M. Frongillo, Bo Waggoner
Last iterate convergence of SGD for Least-Squares in the Interpolation regime. Aditya Vardhan Varre, Loucas Pillaud-Vivien, Nicolas Flammarion
Generative vs. Discriminative: Rethinking The Meta-Continual Learning Mohammadamin Banayeeanzade, Rasoul Mirzaiezadeh, Hosein Hasani, Mahdieh Soleymani
Model, sample, and epoch-wise descents: exact solution of gradient flow in the random feature model Antoine Bodin, Nicolas Macris
Rethinking Graph Transformers with Spectral Attention Devin Kreuzer, Dominique Beaini, Will Hamilton, Vincent Létourneau, Prudencio Tossou
Perceptual Score: What Data Modalities Does Your Model Perceive? Itai Gat, Idan Schwartz, Alex Schwing
PiRank: Scalable Learning To Rank via Differentiable Sorting Robin Swezey, Aditya Grover, Bruno Charron, Stefano Ermon
Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data Liming Jiang, Bo Dai, Wayne Wu, Chen Change Loy
CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei, Kush R Varshney
Iterative Teaching by Label Synthesis Weiyang Liu, Zhen Liu, Hanchen Wang, Liam Paull, Bernhard Schölkopf, Adrian Weller
Variational Diffusion Models Diederik Kingma, Tim Salimans, Ben Poole, Jonathan Ho
FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition Yichong Leng, Xu Tan, Linchen Zhu, Jin Xu, Renqian Luo, Linquan Liu, Tao Qin, Xiangyang Li, Edward Lin, Tie-Yan Liu
Integrated Latent Heterogeneity and Invariance Learning in Kernel Space Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen
Hierarchical Reinforcement Learning with Timed Subgoals Nico Gürtler, Dieter Büchler, Georg Martius
Fair Scheduling for Time-dependent Resources Bo Li, Minming Li, Ruilong Zhang
SNIPS: Solving Noisy Inverse Problems Stochastically Bahjat Kawar, Gregory Vaksman, Michael Elad
Stateful ODE-Nets using Basis Function Expansions Alejandro Queiruga, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney
Beyond the Signs: Nonparametric Tensor Completion via Sign Series Chanwoo Lee, Miaoyan Wang
Functional Variational Inference based on Stochastic Process Generators Chao Ma, José Miguel Hernández-Lobato
TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive? Yuejiang Liu, Parth Kothari, Bastien van Delft, Baptiste Bellot-Gurlet, Taylor Mordan, Alexandre Alahi
Double Machine Learning Density Estimation for Local Treatment Effects with Instruments Yonghan Jung, Jin Tian, Elias Bareinboim
Dirichlet Energy Constrained Learning for Deep Graph Neural Networks Kaixiong Zhou, Xiao Huang, Daochen Zha, Rui Chen, Li Li, Soo-Hyun Choi, Xia Hu
Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives Murtaza Dalal, Deepak Pathak, Ruslan Salakhutdinov
Boosted CVaR Classification Runtian Zhai, Chen Dan, Arun Suggala, J. Zico Kolter, Pradeep K. Ravikumar
Disentangled Contrastive Learning on Graphs Haoyang Li, Xin Wang, Ziwei Zhang, Zehuan Yuan, Hang Li, Wenwu Zhu
Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation Lin Guan, Mudit Verma, Suna (Sihang) Guo, Ruohan Zhang, Subbarao Kambhampati
SOLQ: Segmenting Objects by Learning Queries Bin Dong, Fangao Zeng, Tiancai Wang, Xiangyu Zhang, Yichen Wei
Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty
Best-case lower bounds in online learning Cristóbal Guzmán, Nishant Mehta, Ali Mortazavi
A Comprehensively Tight Analysis of Gradient Descent for PCA Zhiqiang Xu, Ping Li
On Robust Optimal Transport: Computational Complexity and Barycenter Computation Khang Le, Huy Nguyen, Quang M Nguyen, Tung Pham, Hung Bui, Nhat Ho
Asymptotically Best Causal Effect Identification with Multi-Armed Bandits Alan Malek, Silvia Chiappa
Learning rule influences recurrent network representations but not attractor structure in decision-making tasks Brandon McMahan, Michael Kleinman, Jonathan Kao
Few-Shot Segmentation via Cycle-Consistent Transformer Gengwei Zhang, Guoliang Kang, Yi Yang, Yunchao Wei
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks Pál András Papp, Karolis Martinkus, Lukas Faber, Roger Wattenhofer
Photonic Differential Privacy with Direct Feedback Alignment Ruben Ohana, Hamlet Medina, Julien Launay, Alessandro Cappelli, Iacopo Poli, Liva Ralaivola, Alain Rakotomamonjy
Searching Parameterized AP Loss for Object Detection Tao Chenxin, Zizhang Li, Xizhou Zhu, Gao Huang, Yong Liu, jifeng dai
Fair Exploration via Axiomatic Bargaining Jackie Baek, Vivek Farias
Unifying lower bounds on prediction dimension of convex surrogates Jessica Finocchiaro, Rafael M. Frongillo, Bo Waggoner
Ultrahyperbolic Neural Networks Marc Law
NeuroMLR: Robust & Reliable Route Recommendation on Road Networks Jayant Jain, Vrittika Bagadia, Sahil Manchanda, Sayan Ranu
Risk Bounds and Calibration for a Smart Predict-then-Optimize Method Heyuan Liu, Paul Grigas
Three-dimensional spike localization and improved motion correction for Neuropixels recordings Julien Boussard, Erdem Varol, Hyun Dong Lee, Nishchal Dethe, Liam Paninski
Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning Hanzhe Hu, Fangyun Wei, Han Hu, Qiwei Ye, Jinshi Cui, Liwei Wang
On the Bias-Variance-Cost Tradeoff of Stochastic Optimization Yifan Hu, Xin Chen, Niao He
Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent Jason Altschuler, Sinho Chewi, Patrik R Gerber, Austin Stromme
Reinforcement Learning in Newcomblike Environments James Bell, Linda Linsefors, Caspar Oesterheld, Joar Skalse
Comprehensive Knowledge Distillation with Causal Intervention Xiang Deng, Zhongfei Zhang
Reinforcement Learning with Latent Flow Wenling Shang, Xiaofei Wang, Aravind Srinivas, Aravind Rajeswaran, Yang Gao, Pieter Abbeel, Misha Laskin
Understanding How Encoder-Decoder Architectures Attend Kyle Aitken, Vinay Ramasesh, Yuan Cao, Niru Maheswaranathan
Latent Execution for Neural Program Synthesis Beyond Domain-Specific Languages Xinyun Chen, Dawn Song, Yuandong Tian
Two steps to risk sensitivity Christopher Gagne, Peter Dayan
DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks Boris van Breugel, Trent Kyono, Jeroen Berrevoets, Mihaela van der Schaar
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization Ondrej Bohdal, Yongxin Yang, Timothy Hospedales
Biological learning in key-value memory networks Danil Tyulmankov, Ching Fang, Annapurna Vadaparty, Guangyu Robert Yang
Correlated Stochastic Block Models: Exact Graph Matching with Applications to Recovering Communities Miklos Racz, Anirudh Sridhar
Twice regularized MDPs and the equivalence between robustness and regularization Esther Derman, Matthieu Geist, Shie Mannor
Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits Reda Ouhamma, Rémy Degenne, Pierre Gaillard, Vianney Perchet
Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs Jiafan He, Dongruo Zhou, Quanquan Gu
Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration Yan Sun, Wenjun Xiong, Faming Liang
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration Shengjia Zhao, Michael Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon
Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks Dmitry Kovalev, Elnur Gasanov, Alexander Gasnikov, Peter Richtarik
Testing Probabilistic Circuits Yash Pralhad Pote, Kuldeep S Meel
Pseudo-Spherical Contrastive Divergence Lantao Yu, Jiaming Song, Yang Song, Stefano Ermon
NORESQA: A Framework for Speech Quality Assessment using Non-Matching References Pranay Manocha, Buye Xu, Anurag Kumar
AFEC: Active Forgetting of Negative Transfer in Continual Learning Liyuan Wang, Mingtian Zhang, Zhongfan Jia, Qian Li, Chenglong Bao, Kaisheng Ma, Jun Zhu, Yi Zhong
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang
SWAD: Domain Generalization by Seeking Flat Minima Junbum Cha, Sanghyuk Chun, Kyungjae Lee, Han-Cheol Cho, Seunghyun Park, Yunsung Lee, Sungrae Park
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long
Predicting Event Memorability from Contextual Visual Semantics Qianli Xu, Fen Fang, Ana Molino, Vigneshwaran Subbaraju, Joo-Hwee Lim
Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning Zixuan Ke, Bing Liu, Nianzu Ma, Hu Xu, Lei Shu
Bandits with many optimal arms Rianne de Heide, James Cheshire, Pierre Ménard, Alexandra Carpentier
Combiner: Full Attention Transformer with Sparse Computation Cost Hongyu Ren, Hanjun Dai, Zihang Dai, Mengjiao Yang, Jure Leskovec, Dale Schuurmans, Bo Dai
Geometry Processing with Neural Fields Guandao Yang, Serge Belongie, Bharath Hariharan, Vladlen Koltun
Contextual Recommendations and Low-Regret Cutting-Plane Algorithms Sreenivas Gollapudi, Guru Guruganesh, Kostas Kollias, Pasin Manurangsi, Renato Leme, Jon Schneider
Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network Xiaolin Hu, Kai Li, Weiyi Zhang, Yi Luo, Jean-Marie Lemercier, Timo Gerkmann
Reinforcement Learning Enhanced Explainer for Graph Neural Networks Caihua Shan, Yifei Shen, Yao Zhang, Xiang Li, Dongsheng Li
NAS-Bench-x11 and the Power of Learning Curves Shen Yan, Colin White, Yash Savani, Frank Hutter
Observation-Free Attacks on Stochastic Bandits Yinglun Xu, Bhuvesh Kumar, Jacob D. Abernethy
Learning Disentangled Behavior Embeddings Changhao Shi, Sivan Schwartz, Shahar Levy, Shay Achvat, Maisan Abboud, Amir Ghanayim, Jackie Schiller, Gal Mishne
The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning Yujin Tang, David Ha
Fast Extra Gradient Methods for Smooth Structured Nonconvex-Nonconcave Minimax Problems Sucheol Lee, Donghwan Kim
Analysis of Sensing Spectral for Signal Recovery under a Generalized Linear Model Junjie Ma, Ji Xu, Arian Maleki
Revisiting ResNets: Improved Training and Scaling Strategies Irwan Bello, William Fedus, Xianzhi Du, Ekin Dogus Cubuk, Aravind Srinivas, Tsung-Yi Lin, Jonathon Shlens, Barret Zoph
Sparse Flows: Pruning Continuous-depth Models Lucas Liebenwein, Ramin Hasani, Alexander Amini, Daniela Rus
Spectrum-to-Kernel Translation for Accurate Blind Image Super-Resolution Guangpin Tao, Xiaozhong Ji, Wenzhuo Wang, Shuo Chen, Chuming Lin, Yun Cao, Tong Lu, Donghao Luo, Ying Tai
On the Rate of Convergence of Regularized Learning in Games: From Bandits and Uncertainty to Optimism and Beyond Angeliki Giannou, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Panayotis Mertikopoulos
SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks Bahare Fatemi, Layla El Asri, Seyed Mehran Kazemi
Aligning Pretraining for Detection via Object-Level Contrastive Learning Fangyun Wei, Yue Gao, Zhirong Wu, Han Hu, Stephen Lin
Double/Debiased Machine Learning for Dynamic Treatment Effects Greg Lewis, Vasilis Syrgkanis
Local Disentanglement in Variational Auto-Encoders Using Jacobian $L_1$ Regularization Travers Rhodes, Daniel D. Lee
Design of Experiments for Stochastic Contextual Linear Bandits Andrea Zanette, Kefan Dong, Jonathan N Lee, Emma Brunskill
Encoding Spatial Distribution of Convolutional Features for Texture Representation Yong Xu, Feng Li, Zhile Chen, Jinxiu Liang, Yuhui Quan
Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds Yujia Huang, Huan Zhang, Yuanyuan Shi, J. Zico Kolter, Anima Anandkumar
Average-Reward Learning and Planning with Options Yi Wan, Abhishek Naik, Rich Sutton
SSAL: Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection Muhammad Akhtar Munir, Muhammad Haris Khan, M. Sarfraz, Mohsen Ali
Counterexample Guided RL Policy Refinement Using Bayesian Optimization Briti Gangopadhyay, Pallab Dasgupta
Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding Shengjie Luo, Shanda Li, Tianle Cai, Di He, Dinglan Peng, Shuxin Zheng, Guolin Ke, Liwei Wang, Tie-Yan Liu
Learning in Non-Cooperative Configurable Markov Decision Processes Giorgia Ramponi, Alberto Maria Metelli, Alessandro Concetti, Marcello Restelli
Identification of Partially Observed Linear Causal Models: Graphical Conditions for the Non-Gaussian and Heterogeneous Cases Jeffrey Adams, Niels Hansen, Kun Zhang
DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer Wenzheng Chen, Joey Litalien, Jun Gao, Zian Wang, Clement Fuji Tsang, Sameh Khamis, Or Litany, Sanja Fidler
Coresets for Time Series Clustering Lingxiao Huang, K Sudhir, Nisheeth Vishnoi
A Variational Perspective on Diffusion-Based Generative Models and Score Matching Chin-Wei Huang, Jae Hyun Lim, Aaron C. Courville
Online Active Learning with Surrogate Loss Functions Giulia DeSalvo, Claudio Gentile, Tobias Sommer Thune
Does Preprocessing Help Training Over-parameterized Neural Networks? Zhao Song, Shuo Yang, Ruizhe Zhang
Causal Influence Detection for Improving Efficiency in Reinforcement Learning Maximilian Seitzer, Bernhard Schölkopf, Georg Martius
LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active Learning Yoon-Yeong Kim, Kyungwoo Song, JoonHo Jang, Il-chul Moon
Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses Haipeng Luo, Chen-Yu Wei, Chung-Wei Lee
Multiclass versus Binary Differentially Private PAC Learning Satchit Sivakumar, Mark Bun, Marco Gaboardi
Adversarially Robust Change Point Detection Mengchu Li, Yi Yu
Cycle Self-Training for Domain Adaptation Hong Liu, Jianmin Wang, Mingsheng Long
Novel Visual Category Discovery with Dual Ranking Statistics and Mutual Knowledge Distillation Bingchen Zhao, Kai Han
Stochastic Anderson Mixing for Nonconvex Stochastic Optimization Fuchao Wei, Chenglong Bao, Yang Liu
Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model Bingyan Wang, Yuling Yan, Jianqing Fan
NN-Baker: A Neural-network Infused Algorithmic Framework for Optimization Problems on Geometric Intersection Graphs Evan McCarty, Qi Zhao, Anastasios Sidiropoulos, Yusu Wang
A Note on Sparse Generalized Eigenvalue Problem Yunfeng Cai, Guanhua Fang, Ping Li
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents Wei Qiu, Xinrun Wang, Runsheng Yu, Rundong Wang, Xu He, Bo An, Svetlana Obraztsova, Zinovi Rabinovich
Optimal Policies Tend To Seek Power Alex Turner, Logan Smith, Rohin Shah, Andrew Critch, Prasad Tadepalli
Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication Łukasz Kuciński, Tomasz Korbak, Paweł Kołodziej, Piotr Miłoś
PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair Zimin Chen, Vincent J Hellendoorn, Pascal Lamblin, Petros Maniatis, Pierre-Antoine Manzagol, Daniel Tarlow, Subhodeep Moitra
COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining Yu Meng, Chenyan Xiong, Payal Bajaj, saurabh tiwary, Paul Bennett, Jiawei Han, XIA SONG
Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization Qi Deng, Wenzhi Gao
XDO: A Double Oracle Algorithm for Extensive-Form Games Stephen McAleer, JB Lanier, Kevin A Wang, Pierre Baldi, Roy Fox
Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations Vihari Piratla, Soumen Chakrabarti, Sunita Sarawagi
A mechanistic multi-area recurrent network model of decision-making Michael Kleinman, Chandramouli Chandrasekaran, Jonathan Kao
Learning to Compose Visual Relations Nan Liu, Shuang Li, Yilun Du, Josh Tenenbaum, Antonio Torralba
Identity testing for Mallows model Róbert Busa-Fekete, Dimitris Fotakis, Balazs Szorenyi, Emmanouil Zampetakis
Bandits with Knapsacks beyond the Worst Case Karthik Abinav Sankararaman, Aleksandrs Slivkins
Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation Yuchao Qin, Fergus Imrie, Alihan Hüyük, Daniel Jarrett, alexander gimson, Mihaela van der Schaar
Change Point Detection via Multivariate Singular Spectrum Analysis Arwa Alanqary, Abdullah Alomar, Devavrat Shah
Meta-learning to Improve Pre-training Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith, Matthew McDermott, David K. Duvenaud
Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem Adarsh Barik, Jean Honorio
Probabilistic Margins for Instance Reweighting in Adversarial Training qizhou wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama
Unbalanced Optimal Transport through Non-negative Penalized Linear Regression Laetitia Chapel, Rémi Flamary, Haoran Wu, Cédric Févotte, Gilles Gasso
The Difficulty of Passive Learning in Deep Reinforcement Learning Georg Ostrovski, Pablo Samuel Castro, Will Dabney
Intriguing Properties of Vision Transformers Muhammad Muzammal Naseer, Kanchana Ranasinghe, Salman H Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang
PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization Benyuan Sun, Hongxing Huo, YI YANG, Bo Bai
Adaptive Diffusion in Graph Neural Networks Jialin Zhao, Yuxiao Dong, Ming Ding, Evgeny Kharlamov, Jie Tang
Recurrent Submodular Welfare and Matroid Blocking Semi-Bandits Orestis Papadigenopoulos, Constantine Caramanis
Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models Yi Sui, Ga Wu, Scott Sanner
Editing a classifier by rewriting its prediction rules Shibani Santurkar, Dimitris Tsipras, Mahalaxmi Elango, David Bau, Antonio Torralba, Aleksander Madry
How Modular should Neural Module Networks Be for Systematic Generalization? Vanessa D'Amario, Tomotake Sasaki, Xavier Boix
Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing Aadarsh Sahoo, Rutav Shah, Rameswar Panda, Kate Saenko, Abir Das
The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization Daniel LeJeune, Hamid Javadi, Richard Baraniuk
Active Learning of Convex Halfspaces on Graphs Maximilian Thiessen, Thomas Gaertner
Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks Yuhang Li, Yufei Guo, Shanghang Zhang, Shikuang Deng, Yongqing Hai, Shi Gu
Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs Nurendra Choudhary, Nikhil Rao, Sumeet Katariya, Karthik Subbian, Chandan Reddy
Black Box Probabilistic Numerics Onur Teymur, Christopher Foley, Philip Breen, Toni Karvonen, Chris J Oates
Interpolation can hurt robust generalization even when there is no noise Konstantin Donhauser, Alexandru Tifrea, Michael Aerni, Reinhard Heckel, Fanny Yang
On the Equivalence between Neural Network and Support Vector Machine Yilan Chen, Wei Huang, Lam Nguyen, Tsui-Wei Weng
Learning Semantic Representations to Verify Hardware Designs Shobha Vasudevan, Wenjie (Joe) Jiang, David Bieber, Rishabh Singh, hamid shojaei, C. Richard Ho, Charles A. Sutton
Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training Minguk Kang, Woohyeon Shim, Minsu Cho, Jaesik Park
Towards a Theoretical Framework of Out-of-Distribution Generalization Haotian Ye, Chuanlong Xie, Tianle Cai, Ruichen Li, Zhenguo Li, Liwei Wang
Slice Sampling Reparameterization Gradients David Zoltowski, Diana Cai, Ryan P. Adams
Multi-Label Learning with Pairwise Relevance Ordering Ming-Kun Xie, Sheng-Jun Huang
Sampling with Trusthworthy Constraints: A Variational Gradient Framework Xingchao Liu, Xin Tong, Qiang Liu
Robust and Decomposable Average Precision for Image Retrieval Elias Ramzi, Nicolas THOME, Clément Rambour, Nicolas Audebert, Xavier Bitot
Fast rates for prediction with limited expert advice El Mehdi Saad, Gilles Blanchard
Probabilistic Transformer For Time Series Analysis Binh Tang, David S Matteson
A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems Yi Ma, Xiaotian Hao, Jianye Hao, Jiawen Lu, Xing Liu, Tong Xialiang, Mingxuan Yuan, Zhigang Li, Jie Tang, Zhaopeng Meng
Spatio-Temporal Variational Gaussian Processes Oliver Hamelijnck, William Wilkinson, Niki Loppi, Arno Solin, Theodoros Damoulas
MERLOT: Multimodal Neural Script Knowledge Models Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, Yejin Choi
Fast Approximate Dynamic Programming for Infinite-Horizon Markov Decision Processes Mohamad Amin Sharifi Kolarijani, Gyula Max, Peyman Mohajerin Esfahani
Adaptive Risk Minimization: Learning to Adapt to Domain Shift Marvin Zhang, Henrik Marklund, Nikita Dhawan, Abhishek Gupta, Sergey Levine, Chelsea Finn
Learning State Representations from Random Deep Action-conditional Predictions Zeyu Zheng, Vivek Veeriah, Risto Vuorio, Richard L Lewis, Satinder P. Singh
Mixability made efficient: Fast online multiclass logistic regression Rémi Jézéquel, Pierre Gaillard, Alessandro Rudi
Tracking People with 3D Representations Jathushan Rajasegaran, Georgios Pavlakos, Angjoo Kanazawa, Jitendra Malik
Off-Policy Risk Assessment in Contextual Bandits Audrey Huang, Liu Leqi, Zachary Lipton, Kamyar Azizzadenesheli
Adaptive Denoising via GainTuning Sreyas Mohan, Joshua L Vincent, Ramon Manzorro, Peter Crozier, Carlos Fernandez-Granda, Eero P. Simoncelli
Optimal Sketching for Trace Estimation Shuli Jiang, Hai Pham, David Woodruff, Richard Zhang
Estimating Multi-cause Treatment Effects via Single-cause Perturbation Zhaozhi Qian, Alicia Curth, Mihaela van der Schaar
Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration Xiao Wang, Hongrui Liu, Chuan Shi, Cheng Yang
Learning Riemannian metric for disease progression modeling Samuel Gruffaz, Pierre-Emmanuel Poulet, Etienne Maheux, Bruno Jedynak, Stanley DURRLEMAN
Bias and variance of the Bayesian-mean decoder Arthur Prat-Carrabin, Michael Woodford
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms Trent Kyono, Yao Zhang, Alexis Bellot, Mihaela van der Schaar
Efficient Training of Visual Transformers with Small Datasets Yahui Liu, Enver Sangineto, Wei Bi, Nicu Sebe, Bruno Lepri, Marco Nadai
Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction Dominik Stöger, Mahdi Soltanolkotabi
Efficient Combination of Rematerialization and Offloading for Training DNNs Olivier Beaumont, Lionel Eyraud-Dubois, Alena Shilova
Particle Cloud Generation with Message Passing Generative Adversarial Networks Raghav Kansal, Javier Duarte, Hao Su, Breno Orzari, Thiago Tomei, Maurizio Pierini, Mary Touranakou, jean-roch vlimant, Dimitrios Gunopulos
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust PointCloud Registration Hao Yu, Fu Li, Mahdi Saleh, Benjamin Busam, Slobodan Ilic
Partial success in closing the gap between human and machine vision Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Tizian Thieringer, Matthias Bethge, Felix A. Wichmann, Wieland Brendel
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan, Raghav Somani, Jae Sung Park, Krishna Pillutla, Prateek Jain, Sham Kakade, Ali Farhadi
Analytic Insights into Structure and Rank of Neural Network Hessian Maps Sidak Pal Singh, Gregor Bachmann, Thomas Hofmann
Well-tuned Simple Nets Excel on Tabular Datasets Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka
POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples Duong Le, Khoi Duc Nguyen, Khoi Nguyen, Quoc-Huy Tran, Rang Nguyen, Binh-Son Hua
Combinatorial Pure Exploration with Bottleneck Reward Function Yihan Du, Yuko Kuroki, Wei Chen
Densely connected normalizing flows Matej Grcić, Ivan Grubišić, Siniša Šegvić
Snowflake: Scaling GNNs to high-dimensional continuous control via parameter freezing Charles Blake, Vitaly Kurin, Maximilian Igl, Shimon Whiteson
Subgame solving without common knowledge Brian Zhang, Tuomas Sandholm
Fair Algorithms for Multi-Agent Multi-Armed Bandits Safwan Hossain, Evi Micha, Nisarg Shah
VAST: Value Function Factorization with Variable Agent Sub-Teams Thomy Phan, Fabian Ritz, Lenz Belzner, Philipp Altmann, Thomas Gabor, Claudia Linnhoff-Popien
On the Stochastic Stability of Deep Markov Models Jan Drgona, Sayak Mukherjee, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar
Multiwavelet-based Operator Learning for Differential Equations Gaurav Gupta, Xiongye Xiao, Paul Bogdan
Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning Aakash Kaku, Sahana Upadhya, Narges Razavian
An Efficient Pessimistic-Optimistic Algorithm for Stochastic Linear Bandits with General Constraints Xin Liu, Bin Li, Pengyi Shi, Lei Ying
Efficiently Learning One Hidden Layer ReLU Networks From Queries Sitan Chen, Adam Klivans, Raghu Meka
Learning Nonparametric Volterra Kernels with Gaussian Processes Magnus Ross, Michael T Smith, Mauricio Álvarez
DiBS: Differentiable Bayesian Structure Learning Lars Lorch, Jonas Rothfuss, Bernhard Schölkopf, Andreas Krause
Nonparametric estimation of continuous DPPs with kernel methods Michaël Fanuel, Rémi Bardenet
FINE Samples for Learning with Noisy Labels Taehyeon Kim, Jongwoo Ko, sangwook Cho, JinHwan Choi, Se-Young Yun
Residual2Vec: Debiasing graph embedding with random graphs Sadamori Kojaku, Jisung Yoon, Isabel Constantino, Yong-Yeol Ahn
Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation Ke Wang, Vidya Muthukumar, Christos Thrampoulidis
Instance-Dependent Bounds for Zeroth-order Lipschitz Optimization with Error Certificates Francois Bachoc, Tom Cesari, Sébastien Gerchinovitz
Training Neural Networks with Fixed Sparse Masks Yi-Lin Sung, Varun Nair, Colin A Raffel
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text Hassan Akbari, Liangzhe Yuan, Rui Qian, Wei-Hong Chuang, Shih-Fu Chang, Yin Cui, Boqing Gong
Analyzing the Generalization Capability of SGLD Using Properties of Gaussian Channels Hao Wang, Yizhe Huang, Rui Gao, Flavio Calmon
Learning to Schedule Heuristics in Branch and Bound Antonia Chmiela, Elias Khalil, Ambros Gleixner, Andrea Lodi, Sebastian Pokutta
On Training Implicit Models Zhengyang Geng, Xin-Yu Zhang, Shaojie Bai, Yisen Wang, Zhouchen Lin
MLP-Mixer: An all-MLP Architecture for Vision Ilya O Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy
A Framework to Learn with Interpretation Jayneel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc
One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective Jiun Tian Hoe, Kam Woh Ng, Tianyu Zhang, Chee Seng Chan, Yi-Zhe Song, Tao Xiang
Fast and accurate randomized algorithms for low-rank tensor decompositions Linjian Ma, Edgar Solomonik
Communication-efficient SGD: From Local SGD to One-Shot Averaging Artin Spiridonoff, Alex Olshevsky, Yannis Paschalidis
Memory Efficient Meta-Learning with Large Images John Bronskill, Daniela Massiceti, Massimiliano Patacchiola, Katja Hofmann, Sebastian Nowozin, Richard Turner
On the Power of Differentiable Learning versus PAC and SQ Learning Emmanuel Abbe, Pritish Kamath, Eran Malach, Colin Sandon, Nathan Srebro
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression Will Stephenson, Zachary Frangella, Madeleine Udell, Tamara Broderick
Adaptive Proximal Gradient Methods for Structured Neural Networks Jihun Yun, Aurelie C. Lozano, Eunho Yang
Discovering and Achieving Goals via World Models Russell Mendonca, Oleh Rybkin, Kostas Daniilidis, Danijar Hafner, Deepak Pathak
Understanding and Improving Early Stopping for Learning with Noisy Labels Yingbin Bai, Erkun Yang, Bo Han, Yanhua Yang, Jiatong Li, Yinian Mao, Gang Niu, Tongliang Liu
Distributionally Robust Imitation Learning Mohammad Ali Bashiri, Brian Ziebart, Xinhua Zhang
On the Power of Edge Independent Graph Models Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos Tsourakakis
Stochastic Online Linear Regression: the Forward Algorithm to Replace Ridge Reda Ouhamma, Odalric-Ambrym Maillard, Vianney Perchet
Dr Jekyll & Mr Hyde: the strange case of off-policy policy updates Romain Laroche, Remi Tachet des Combes
Understanding Adaptive, Multiscale Temporal Integration In Deep Speech Recognition Systems Menoua Keshishian, Samuel Norman-Haignere, Nima Mesgarani
VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer Zineng Tang, Jaemin Cho, Hao Tan, Mohit Bansal
Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess Reid McIlroy-Young, Yu Wang, Siddhartha Sen, Jon M. Kleinberg, Ashton Anderson
Coupled Gradient Estimators for Discrete Latent Variables Zhe Dong, Andriy Mnih, George Tucker
AutoGEL: An Automated Graph Neural Network with Explicit Link Information Zhili Wang, Shimin DI, Lei Chen
RL for Latent MDPs: Regret Guarantees and a Lower Bound Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor
Adaptive Sampling for Minimax Fair Classification Shubhanshu Shekhar, Greg Fields, Mohammad Ghavamzadeh, Tara Javidi
Structured in Space, Randomized in Time: Leveraging Dropout in RNNs for Efficient Training Anup Sarma, Sonali Singh, Huaipan Jiang, Rui Zhang, Mahmut Kandemir, Chita Das
Variational Continual Bayesian Meta-Learning Qiang Zhang, Jinyuan Fang, Zaiqiao Meng, Shangsong Liang, Emine Yilmaz
Recognizing Vector Graphics without Rasterization XINYANG JIANG, LU LIU, Caihua Shan, Yifei Shen, Xuanyi Dong, Dongsheng Li
On Episodes, Prototypical Networks, and Few-Shot Learning Steinar Laenen, Luca Bertinetto
Pointwise Bounds for Distribution Estimation under Communication Constraints Wei-Ning Chen, Peter Kairouz, Ayfer Ozgur
CHIP: CHannel Independence-based Pruning for Compact Neural Networks Yang Sui, Miao Yin, Yi Xie, Huy Phan, Saman Aliari Zonouz, Bo Yuan
Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis Sangjoon Park, Gwanghyun Kim, Jeongsol Kim, Boah Kim, Jong Chul Ye
Active Offline Policy Selection Ksenia Konyushova, Yutian Chen, Thomas Paine, Caglar Gulcehre, Cosmin Paduraru, Daniel J Mankowitz, Misha Denil, Nando de Freitas
Unsupervised Representation Transfer for Small Networks: I Believe I Can Distill On-the-Fly Hee Min Choi, Hyoa Kang, Dokwan Oh
Understanding Bandits with Graph Feedback Houshuang Chen, zengfeng Huang, Shuai Li, Chihao Zhang
Information-theoretic generalization bounds for black-box learning algorithms Hrayr Harutyunyan, Maxim Raginsky, Greg Ver Steeg, Aram Galstyan
Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation Qiming Hu, Xiaojie Guo
Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding Tengwei Song, Jie Luo, Lei Huang
Planning from Pixels in Environments with Combinatorially Hard Search Spaces Marco Bagatella, Miroslav Olšák, Michal Rolínek, Georg Martius
PLUGIn: A simple algorithm for inverting generative models with recovery guarantees Babhru Joshi, Xiaowei Li, Yaniv Plan, Ozgur Yilmaz
Modular Gaussian Processes for Transfer Learning Pablo Moreno-Muñoz, Antonio Artes, Mauricio Álvarez
Neural Human Performer: Learning Generalizable Radiance Fields for Human Performance Rendering Youngjoong Kwon, Dahun Kim, Duygu Ceylan, Henry Fuchs
Locally differentially private estimation of functionals of discrete distributions Cristina Butucea, Yann ISSARTEL
Asymptotics of representation learning in finite Bayesian neural networks Jacob Zavatone-Veth, Abdulkadir Canatar, Ben Ruben, Cengiz Pehlevan
Adaptive Ensemble Q-learning: Minimizing Estimation Bias via Error Feedback Hang Wang, Sen Lin, Junshan Zhang
Domain Adaptation with Invariant Representation Learning: What Transformations to Learn? Petar Stojanov, Zijian Li, Mingming Gong, Ruichu Cai, Jaime G. Carbonell, Kun Zhang
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation Yusuke Tashiro, Jiaming Song, Yang Song, Stefano Ermon
Causal Bandits with Unknown Graph Structure Yangyi Lu, Amirhossein Meisami, Ambuj Tewari
Piper: Multidimensional Planner for DNN Parallelization Jakub M Tarnawski, Deepak Narayanan, Amar Phanishayee
Causal Effect Inference for Structured Treatments Jean Kaddour, Yuchen Zhu, Qi Liu, Matt J Kusner, Ricardo Silva
Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging Ali Hashemi, Yijing Gao, Chang Cai, Sanjay Ghosh, Klaus-Robert Müller, Srikantan S. Nagarajan, Stefan Haufe
Topological Attention for Time Series Forecasting Sebastian Zeng, Florian Graf, Christoph Hofer, Roland Kwitt
Local Signal Adaptivity: Provable Feature Learning in Neural Networks Beyond Kernels Stefani Karp, Ezra Winston, Yuanzhi Li, Aarti Singh
IA-RED$^2$: Interpretability-Aware Redundancy Reduction for Vision Transformers Bowen Pan, Rameswar Panda, Yifan Jiang, Zhangyang Wang, Rogerio Feris, Aude Oliva
Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding Terrell Mundhenk, Mikel Landajuela, Ruben Glatt, Claudio P Santiago, Daniel faissol, Brenden K Petersen
Choose a Transformer: Fourier or Galerkin Shuhao Cao
A Causal Lens for Controllable Text Generation Zhiting Hu, Li Erran Li
Differentially Private Multi-Armed Bandits in the Shuffle Model Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer
Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions Lijun Zhang, Guanghui Wang, Wei-Wei Tu, Wei Jiang, Zhi-Hua Zhou
Learning Hard Optimization Problems: A Data Generation Perspective James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck
Canonical Capsules: Self-Supervised Capsules in Canonical Pose Weiwei Sun, Andrea Tagliasacchi, Boyang Deng, Sara Sabour, Soroosh Yazdani, Geoffrey E. Hinton, Kwang Moo Yi
Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning Timo Milbich, Karsten Roth, Samarth Sinha, Ludwig Schmidt, Marzyeh Ghassemi, Bjorn Ommer
Dynamics-regulated kinematic policy for egocentric pose estimation Zhengyi Luo, Ryo Hachiuma, Ye Yuan, Kris Kitani
Never Go Full Batch (in Stochastic Convex Optimization) Idan Amir, Yair Carmon, Tomer Koren, Roi Livni
Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning) El Mahdi El-Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui, Arsany Guirguis, Lê-Nguyên Hoang, Sébastien Rouault
Not All Low-Pass Filters are Robust in Graph Convolutional Networks Heng Chang, Yu Rong, Tingyang Xu, Yatao Bian, Shiji Zhou, Xin Wang, Junzhou Huang, Wenwu Zhu
Counterfactual Maximum Likelihood Estimation for Training Deep Networks Xinyi Wang, Wenhu Chen, Michael Saxon, William Yang Wang
Robust Optimization for Multilingual Translation with Imbalanced Data Xian Li, Hongyu Gong
A/B/n Testing with Control in the Presence of Subpopulations Yoan Russac, Christina Katsimerou, Dennis Bohle, Olivier Cappé, Aurélien Garivier, Wouter M. Koolen
Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural Networks Giora Simchoni, Saharon Rosset
Learning Debiased Representation via Disentangled Feature Augmentation Jungsoo Lee, Eungyeup Kim, Juyoung Lee, Jihyeon Lee, Jaegul Choo
Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning Jiani Huang, Ziyang Li, Binghong Chen, Karan Samel, Mayur Naik, Le Song, Xujie Si
Learning to Synthesize Programs as Interpretable and Generalizable Policies Dweep Trivedi, Jesse Zhang, Shao-Hua Sun, Joseph J. Lim
The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning Shahab Bakhtiari, Patrick Mineault, Timothy Lillicrap, Christopher Pack, Blake Richards
Adversarial Training Helps Transfer Learning via Better Representations Zhun Deng, Linjun Zhang, Kailas Vodrahalli, Kenji Kawaguchi, James Y Zou
Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning Maxwell Nye, Michael Tessler, Josh Tenenbaum, Brenden M Lake
Learning the optimal Tikhonov regularizer for inverse problems Giovanni S Alberti, Ernesto De Vito, Matti Lassas, Luca Ratti, Matteo Santacesaria
NovelD: A Simple yet Effective Exploration Criterion Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E Gonzalez, Yuandong Tian
On Margin-Based Cluster Recovery with Oracle Queries Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice
Multi-Scale Representation Learning on Proteins Vignesh Ram Somnath, Charlotte Bunne, Andreas Krause
Sparse Quadratic Optimisation over the Stiefel Manifold with Application to Permutation Synchronisation Florian Bernard, Daniel Cremers, Johan Thunberg
Second-Order Neural ODE Optimizer Guan-Horng Liu, Tianrong Chen, Evangelos Theodorou
Graph Neural Networks with Local Graph Parameters Pablo Barceló, Floris Geerts, Juan Reutter, Maksimilian Ryschkov
Closing the Gap: Tighter Analysis of Alternating Stochastic Gradient Methods for Bilevel Problems Tianyi Chen, Yuejiao Sun, Wotao Yin
Dense Unsupervised Learning for Video Segmentation Nikita Araslanov, Simone Schaub-Meyer, Stefan Roth
Charting and Navigating the Space of Solutions for Recurrent Neural Networks Elia Turner, Kabir V Dabholkar, Omri Barak
Fast Training Method for Stochastic Compositional Optimization Problems Hongchang Gao, Heng Huang
Dual-stream Network for Visual Recognition Mingyuan Mao, peng gao, Renrui Zhang, Honghui Zheng, Teli Ma, Yan Peng, Errui Ding, Baochang Zhang, Shumin Han
Estimating High Order Gradients of the Data Distribution by Denoising Chenlin Meng, Yang Song, Wenzhe Li, Stefano Ermon
Machine versus Human Attention in Deep Reinforcement Learning Tasks Suna (Sihang) Guo, Ruohan Zhang, Bo Liu, Yifeng Zhu, Dana Ballard, Mary M. Hayhoe, Peter Stone
Reusing Combinatorial Structure: Faster Iterative Projections over Submodular Base Polytopes Jai Moondra, Hassan Mortagy, Swati Gupta
Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes Sara Sangalli, Ertunc Erdil, Andeas Hötker, Olivio Donati, Ender Konukoglu
Collapsed Variational Bounds for Bayesian Neural Networks Marcin Tomczak, Siddharth Swaroop, Andrew Foong, Richard Turner
Consistent Estimation for PCA and Sparse Regression with Oblivious Outliers Tommaso d'Orsi, Chih-Hung Liu, Rajai Nasser, Gleb Novikov, David Steurer, Stefan Tiegel
Offline Constrained Multi-Objective Reinforcement Learning via Pessimistic Dual Value Iteration Runzhe Wu, Yufeng Zhang, Zhuoran Yang, Zhaoran Wang
Absolute Neighbour Difference based Correlation Test for Detecting Heteroscedastic Relationships Lifeng Zhang
Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks Shibo Li, Robert Kirby, Shandian Zhe
Mastering Atari Games with Limited Data Weirui Ye, Shaohuai Liu, Thanard Kurutach, Pieter Abbeel, Yang Gao
Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification Clémence Réda, Andrea Tirinzoni, Rémy Degenne
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine
Set Prediction in the Latent Space Konpat Preechakul, Chawan Piansaddhayanon, Burin Naowarat, Tirasan Khandhawit, Sira Sriswasdi, Ekapol Chuangsuwanich
Best of Both Worlds: Practical and Theoretically Optimal Submodular Maximization in Parallel Yixin Chen, Tonmoy Dey, Alan Kuhnle
Fine-grained Generalization Analysis of Inductive Matrix Completion Antoine Ledent, Rodrigo Alves, Yunwen Lei, Marius Kloft
Learning Frequency Domain Approximation for Binary Neural Networks Yixing Xu, Kai Han, Chang Xu, Yehui Tang, Chunjing XU, Yunhe Wang
Reformulating Zero-shot Action Recognition for Multi-label Actions Alec Kerrigan, Kevin Duarte, Yogesh Rawat, Mubarak Shah
Optimal Best-Arm Identification Methods for Tail-Risk Measures Shubhada Agrawal, Wouter M. Koolen, Sandeep Juneja
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision Irina Higgins, Peter Wirnsberger, Andrew Jaegle, Aleksandar Botev
Learning with Holographic Reduced Representations Ashwinkumar Ganesan, Hang Gao, Sunil Gandhi, Edward Raff, Tim Oates, James Holt, Mark McLean
Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violations Yuping Luo, Tengyu Ma
On the Second-order Convergence Properties of Random Search Methods Aurelien Lucchi, Antonio Orvieto, Adamos Solomou
Noether’s Learning Dynamics: Role of Symmetry Breaking in Neural Networks Hidenori Tanaka, Daniel Kunin
A Theory of the Distortion-Perception Tradeoff in Wasserstein Space Dror Freirich, Tomer Michaeli, Ron Meir
Neural Production Systems Anirudh Goyal ALIAS PARTH GOYAL, Aniket Didolkar, Nan Rosemary Ke, Charles Blundell, Philippe Beaudoin, Nicolas Heess, Michael Mozer, Yoshua Bengio
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization Mher Safaryan, Filip Hanzely, Peter Richtarik
Increasing Liquid State Machine Performance with Edge-of-Chaos Dynamics Organized by Astrocyte-modulated Plasticity Vladimir Ivanov, Konstantinos Michmizos
Fair Sortition Made Transparent Bailey Flanigan, Gregory Kehne, Ariel D Procaccia
A Max-Min Entropy Framework for Reinforcement Learning Seungyul Han, Youngchul Sung
Reward is enough for convex MDPs Tom Zahavy, Brendan O'Donoghue, Guillaume Desjardins, Satinder P. Singh
Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with Weak Mixing Time Bounds Shahrzad Haddadan, Yue Zhuang, Cyrus Cousins, Eli Upfal
Does enforcing fairness mitigate biases caused by subpopulation shift? Subha Maity, Debarghya Mukherjee, Mikhail Yurochkin, Yuekai Sun
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods Desi R Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Thomas Rainforth
Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games Yu Bai, Chi Jin, Huan Wang, Caiming Xiong
Non-approximate Inference for Collective Graphical Models on Path Graphs via Discrete Difference of Convex Algorithm Yasunori Akagi, Naoki Marumo, Hideaki Kim, Takeshi Kurashima, Hiroyuki Toda
Implicit Task-Driven Probability Discrepancy Measure for Unsupervised Domain Adaptation Mao Li, Kaiqi Jiang, Xinhua Zhang
SBO-RNN: Reformulating Recurrent Neural Networks via Stochastic Bilevel Optimization Ziming Zhang, Yun Yue, Guojun Wu, Yanhua Li, Haichong Zhang
Navigating to the Best Policy in Markov Decision Processes Aymen Al Marjani, Aurélien Garivier, Alexandre Proutiere
A Faster Decentralized Algorithm for Nonconvex Minimax Problems Wenhan Xian, Feihu Huang, Yanfu Zhang, Heng Huang
Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis Qi CHEN, Changjian Shui, Mario Marchand
ReLU Regression with Massart Noise Ilias Diakonikolas, Jong Ho Park, Christos Tzamos
Identification of the Generalized Condorcet Winner in Multi-dueling Bandits Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier
Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch Luca Viano, Yu-Ting Huang, Parameswaran Kamalaruban, Adrian Weller, Volkan Cevher
Re-ranking for image retrieval and transductive few-shot classification Xi SHEN, Yang Xiao, Shell Xu Hu, Othman Sbai, Mathieu Aubry
Post-processing for Individual Fairness Felix Petersen, Debarghya Mukherjee, Yuekai Sun, Mikhail Yurochkin
OpenMatch: Open-Set Semi-supervised Learning with Open-set Consistency Regularization Kuniaki Saito, Donghyun Kim, Kate Saenko
End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering Devendra Singh, Siva Reddy, Will Hamilton, Chris Dyer, Dani Yogatama
Fast Algorithms for $L_\infty$-constrained S-rectangular Robust MDPs Bahram Behzadian, Marek Petrik, Chin Pang Ho
Instance-optimal Mean Estimation Under Differential Privacy Ziyue Huang, Yuting Liang, Ke Yi
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis Thomas FEL, Remi Cadene, Mathieu Chalvidal, Matthieu Cord, David Vigouroux, Thomas Serre
PatchGame: Learning to Signal Mid-level Patches in Referential Games Kamal Gupta, Gowthami Somepalli, Anubhav Anubhav, Vinoj Yasanga Jayasundara Magalle Hewa, Matthias Zwicker, Abhinav Shrivastava
Implicit Generative Copulas Tim Janke, Mohamed Ghanmi, Florian Steinke
Tensor Normal Training for Deep Learning Models Yi Ren, Donald Goldfarb
Unintended Selection: Persistent Qualification Rate Disparities and Interventions Reilly Raab, Yang Liu
Revisiting 3D Object Detection From an Egocentric Perspective Boyang Deng, Charles R Qi, Mahyar Najibi, Thomas Funkhouser, Yin Zhou, Dragomir Anguelov
Optimizing Information-theoretical Generalization Bound via Anisotropic Noise of SGLD Bohan Wang, Huishuai Zhang, Jieyu Zhang, Qi Meng, Wei Chen, Tie-Yan Liu
Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning Sen Cui, Weishen Pan, Jian Liang, Changshui Zhang, Fei Wang
A Mathematical Framework for Quantifying Transferability in Multi-source Transfer Learning Xinyi Tong, Xiangxiang Xu, Shao-Lun Huang, Lizhong Zheng
Morié Attack (MA): A New Potential Risk of Screen Photos Dantong Niu, Ruohao Guo, Yisen Wang
Fast Bayesian Inference for Gaussian Cox Processes via Path Integral Formulation Hideaki Kim
Lattice partition recovery with dyadic CART OSCAR HERNAN MADRID PADILLA, Yi Yu, Alessandro Rinaldo
Robust Deep Reinforcement Learning through Adversarial Loss Tuomas Oikarinen, Wang Zhang, Alexandre Megretski, Luca Daniel, Tsui-Wei Weng
Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature Kefan Dong, Jiaqi Yang, Tengyu Ma
You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu
Learning to delegate for large-scale vehicle routing Sirui Li, Zhongxia Yan, Cathy Wu
Effective Meta-Regularization by Kernelized Proximal Regularization Weisen Jiang, James T. Kwok, Yu Zhang
Towards Context-Agnostic Learning Using Synthetic Data Charles Jin, Martin Rinard
Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers Jeffrey Negrea, Blair Bilodeau, Nicolò Campolongo, Francesco Orabona, Dan Roy
Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering Yu Shen, Laura Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming Lin
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation Liyuan Xu, Heishiro Kanagawa, Arthur Gretton
Certifying Robustness to Programmable Data Bias in Decision Trees Anna Meyer, Aws Albarghouthi, Loris D'Antoni
TöRF: Time-of-Flight Radiance Fields for Dynamic Scene View Synthesis Benjamin Attal, Eliot Laidlaw, Aaron Gokaslan, Changil Kim, Christian Richardt, James Tompkin, Matthew O'Toole
Sequence-to-Sequence Learning with Latent Neural Grammars Yoon Kim
Exploration-Exploitation in Multi-Agent Competition: Convergence with Bounded Rationality Stefanos Leonardos, Georgios Piliouras, Kelly Spendlove
Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems Atara Kaplan, Dan Garber
Which Mutual-Information Representation Learning Objectives are Sufficient for Control? Kate Rakelly, Abhishek Gupta, Carlos Florensa, Sergey Levine
A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition Junjiao Tian, Dylan Yung, Yen-Chang Hsu, Zsolt Kira
Towards a Unified Information-Theoretic Framework for Generalization Mahdi Haghifam, Gintare Karolina Dziugaite, Shay Moran, Dan Roy
Bayesian decision-making under misspecified priors with applications to meta-learning Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel J. Hsu, Thodoris Lykouris, Miro Dudik, Robert E. Schapire
Neural Trees for Learning on Graphs Rajat Talak, Siyi Hu, Lisa Peng, Luca Carlone
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization Pranav Subramani, Nicholas Vadivelu, Gautam Kamath
The effectiveness of feature attribution methods and its correlation with automatic evaluation scores Giang Nguyen, Daeyoung Kim, Anh Nguyen
Coordinated Proximal Policy Optimization Zifan Wu, Chao Yu, Deheng Ye, Junge Zhang, haiyin piao, Hankz Hankui Zhuo
Unbiased Classification through Bias-Contrastive and Bias-Balanced Learning Youngkyu Hong, Eunho Yang
Learning from Inside: Self-driven Siamese Sampling and Reasoning for Video Question Answering Weijiang Yu, Haoteng Zheng, Mengfei Li, Lei Ji, Lijun Wu, Nong Xiao, Nan Duan
Identification and Estimation of Joint Probabilities of Potential Outcomes in Observational Studies with Covariate Information Ryusei Shingaki, manabu kuroki
Online false discovery rate control for anomaly detection in time series Quentin Rebjock, Baris Kurt, Tim Januschowski, Laurent Callot
Pragmatic Image Compression for Human-in-the-Loop Decision-Making Sid Reddy, Anca Dragan, Sergey Levine
Generalized Linear Bandits with Local Differential Privacy Yuxuan Han, Zhipeng Liang, Yang Wang, Jiheng Zhang
On the Algorithmic Stability of Adversarial Training Yue Xing, Qifan Song, Guang Cheng
Width-based Lookaheads with Learnt Base Policies and Heuristics Over the Atari-2600 Benchmark Stefan O'Toole, Nir Lipovetzky, Miquel Ramirez, Adrian Pearce
Characterizing possible failure modes in physics-informed neural networks Aditi Krishnapriyan, Amir Gholami, Shandian Zhe, Robert Kirby, Michael W. Mahoney
Artistic Style Transfer with Internal-external Learning and Contrastive Learning Haibo Chen, lei zhao, Zhizhong Wang, Huiming Zhang, Zhiwen Zuo, Ailin Li, Wei Xing, Dongming Lu
Fast Abductive Learning by Similarity-based Consistency Optimization Yu-Xuan Huang, Wang-Zhou Dai, Le-Wen Cai, Stephen H Muggleton, Yuan Jiang
To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs Thomas Scialom, Paul-Alexis Dray, Jacopo Staiano, Sylvain Lamprier, Benjamin Piwowarski
Shapley Residuals: Quantifying the limits of the Shapley value for explanations Indra Kumar, Carlos Scheidegger, Suresh Venkatasubramanian, Sorelle Friedler
The Elastic Lottery Ticket Hypothesis Xiaohan Chen, Yu Cheng, Shuohang Wang, Zhe Gan, Jingjing Liu, Zhangyang Wang
Joint Inference for Neural Network Depth and Dropout Regularization Kishan K C, Rui Li, MohammadMahdi Gilany
Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows Brendan Ross, Jesse Cresswell
The Limits of Optimal Pricing in the Dark Quinlan Dawkins, Minbiao Han, Haifeng Xu
No RL, No Simulation: Learning to Navigate without Navigating Meera Hahn, Devendra Singh Chaplot, Shubham Tulsiani, Mustafa Mukadam, James M. Rehg, Abhinav Gupta
Analogous to Evolutionary Algorithm: Designing a Unified Sequence Model Jiangning Zhang, Chao Xu, Jian Li, Wenzhou Chen, Yabiao Wang, Ying Tai, Shuo Chen, Chengjie Wang, Feiyue Huang, Yong Liu
Improving Compositionality of Neural Networks by Decoding Representations to Inputs Mike Wu, Noah Goodman, Stefano Ermon
The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle Fang Kong, Yueran Yang, Wei Chen, Shuai Li
Universal Semi-Supervised Learning Zhuo Huang, Chao Xue, Bo Han, Jian Yang, Chen Gong
Improving Deep Learning Interpretability by Saliency Guided Training Aya Abdelsalam Ismail, Hector Corrada Bravo, Soheil Feizi
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data Alicia Curth, Changhee Lee, Mihaela van der Schaar
Optimal Rates for Nonparametric Density Estimation under Communication Constraints Jayadev Acharya, Clement Canonne, Aditya Vikram Singh, Himanshu Tyagi
Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery Lijun Ding, Liwei Jiang, Yudong Chen, Qing Qu, Zhihui Zhu
Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings Lili Chen, Kimin Lee, Aravind Srinivas, Pieter Abbeel
Learning Generalized Gumbel-max Causal Mechanisms Guy Lorberbom, Daniel D. Johnson, Chris J Maddison, Daniel Tarlow, Tamir Hazan
Bandit Learning with Delayed Impact of Actions Wei Tang, Chien-Ju Ho, Yang Liu
A Stochastic Newton Algorithm for Distributed Convex Optimization Brian Bullins, Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E Woodworth
Are Transformers more robust than CNNs? Yutong Bai, Jieru Mei, Alan L. Yuille, Cihang Xie
Towards Sharper Generalization Bounds for Structured Prediction Shaojie Li, Yong Liu
Automated Discovery of Adaptive Attacks on Adversarial Defenses Chengyuan Yao, Pavol Bielik, Petar Tsankov, Martin Vechev
PolarStream: Streaming Object Detection and Segmentation with Polar Pillars Qi Chen, Sourabh Vora, Oscar Beijbom
Representation Costs of Linear Neural Networks: Analysis and Design Zhen Dai, Mina Karzand, Nathan Srebro
Teaching via Best-Case Counterexamples in the Learning-with-Equivalence-Queries Paradigm Akash Kumar, Yuxin Chen, Adish Singla
Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social Media Yiyue Qian, Yiming Zhang, Yanfang (Fa Ye, Chuxu Zhang
Curriculum Disentangled Recommendation with Noisy Multi-feedback Hong Chen, Yudong Chen, Xin Wang, Ruobing Xie, Rui Wang, Feng Xia, Wenwu Zhu
Interpretable agent communication from scratch (with a generic visual processor emerging on the side) Roberto Dessi, Eugene Kharitonov, Baroni Marco
MAU: A Motion-Aware Unit for Video Prediction and Beyond Zheng Chang, Xinfeng Zhang, Shanshe Wang, Siwei Ma, Yan Ye, Xiang Xinguang, Wen Gao
Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement Learning Christopher Hoang, Sungryull Sohn, Jongwook Choi, Wilka Carvalho, Honglak Lee
Streaming Belief Propagation for Community Detection Yuchen Wu, Jakab Tardos, Mohammadhossein Bateni, André Linhares, Filipe Miguel Goncalves de Almeida, Andrea Montanari, Ashkan Norouzi-Fard
The staircase property: How hierarchical structure can guide deep learning Emmanuel Abbe, Enric Boix-Adsera, Matthew S Brennan, Guy Bresler, Dheeraj Nagaraj
MagNet: A Neural Network for Directed Graphs Xitong Zhang, Yixuan He, Nathan Brugnone, Michael Perlmutter, Matthew Hirn
Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning Hayeon Lee, Sewoong Lee, Song Chong, Sung Ju Hwang
Topological Relational Learning on Graphs Yuzhou Chen, Baris Coskunuzer, Yulia Gel
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks Pascal Esser, Leena Chennuru Vankadara, Debarghya Ghoshdastidar
Federated Linear Contextual Bandits Ruiquan Huang, Weiqiang Wu, Jing Yang, Cong Shen
Least Square Calibration for Peer Reviews Sijun Tan, Jibang Wu, Xiaohui Bei, Haifeng Xu
Scaling Up Exact Neural Network Compression by ReLU Stability Thiago Serra, Xin Yu, Abhinav Kumar, Srikumar Ramalingam
Passive attention in artificial neural networks predicts human visual selectivity Thomas Langlois, Haicheng Zhao, Erin Grant, Ishita Dasgupta, Tom Griffiths, Nori Jacoby
GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction Longyuan Li, Jian Yao, Li Wenliang, Tong He, Tianjun Xiao, Junchi Yan, David P. Wipf, Zheng Zhang
Instance-Dependent Partial Label Learning Ning Xu, Congyu Qiao, Xin Geng, Min-Ling Zhang
Deep Learning with Label Differential Privacy Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang
Semialgebraic Representation of Monotone Deep Equilibrium Models and Applications to Certification Tong Chen, Jean B Lasserre, Victor Magron, Edouard Pauwels
The Role of Global Labels in Few-Shot Classification and How to Infer Them Ruohan Wang, Massimiliano Pontil, Carlo Ciliberto
NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, Wenping Wang
Improved Guarantees for Offline Stochastic Matching via new Ordered Contention Resolution Schemes Brian Brubach, Nathaniel Grammel, Will Ma, Aravind Srinivasan
UFC-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis Zhu Zhang, Jianxin Ma, Chang Zhou, Rui Men, Zhikang Li, Ming Ding, Jie Tang, Jingren Zhou, Hongxia Yang
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies Tim Seyde, Igor Gilitschenski, Wilko Schwarting, Bartolomeo Stellato, Martin Riedmiller, Markus Wulfmeier, Daniela Rus
Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics Mixture Suyoung Lee, Sae-Young Chung
Localization with Sampling-Argmax Jiefeng Li, Tong Chen, Ruiqi Shi, Yujing Lou, Yong-Lu Li, Cewu Lu
Improved Regularization and Robustness for Fine-tuning in Neural Networks Dongyue Li, Hongyang Zhang
BARTScore: Evaluating Generated Text as Text Generation Weizhe Yuan, Graham Neubig, Pengfei Liu
An analysis of Ermakov-Zolotukhin quadrature using kernels Ayoub Belhadji
Towards Understanding Why Lookahead Generalizes Better Than SGD and Beyond Pan Zhou, Hanshu Yan, Xiaotong Yuan, Jiashi Feng, Shuicheng Yan
Online Market Equilibrium with Application to Fair Division Yuan Gao, Alex Peysakhovich, Christian Kroer
Dynamic Resolution Network Mingjian Zhu, Kai Han, Enhua Wu, Qiulin Zhang, Ying Nie, Zhenzhong Lan, Yunhe Wang
Gauge Equivariant Transformer Lingshen He, Yiming Dong, Yisen Wang, Dacheng Tao, Zhouchen Lin
Unsupervised Object-Based Transition Models For 3D Partially Observable Environments Antonia Creswell, Rishabh Kabra, Chris Burgess, Murray Shanahan
Robust Contrastive Learning Using Negative Samples with Diminished Semantics Songwei Ge, Shlok Mishra, Chun-Liang Li, Haohan Wang, David Jacobs
General Low-rank Matrix Optimization: Geometric Analysis and Sharper Bounds Haixiang Zhang, Yingjie Bi, Javad Lavaei
Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, Yoshua Bengio
Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning Tengyang Xie, Nan Jiang, Huan Wang, Caiming Xiong, Yu Bai
Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation Jungbeom Lee, Jooyoung Choi, Jisoo Mok, Sungroh Yoon
SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs Ayush Sekhari, Karthik Sridharan, Satyen Kale
AC-GC: Lossy Activation Compression with Guaranteed Convergence R David Evans, Tor Aamodt
Label Noise SGD Provably Prefers Flat Global Minimizers Alex Damian, Tengyu Ma, Jason Lee
Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks Sandesh Kamath, Amit Deshpande, Subrahmanyam Kambhampati Venkata, Vineeth N Balasubramanian
Universal Off-Policy Evaluation Yash Chandak, Scott Niekum, Bruno da Silva, Erik Learned-Miller, Emma Brunskill, Philip S. Thomas
A Non-commutative Extension of Lee-Seung's Algorithm for Positive Semidefinite Factorizations Yong Sheng Soh, Antonios Varvitsiotis
Efficiently Identifying Task Groupings for Multi-Task Learning Chris Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn
Instance-Conditioned GAN Arantxa Casanova, Marlene Careil, Jakob J. Verbeek, Michal Drozdzal, Adriana Romero Soriano
DeepSITH: Efficient Learning via Decomposition of What and When Across Time Scales Brandon Jacques, Zoran Tiganj, Marc Howard, Per B Sederberg
A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning Weishi Shi, Dayou Yu, Qi Yu
Differentially Private Empirical Risk Minimization under the Fairness Lens Cuong Tran, My Dinh, Ferdinando Fioretto
A Unified View of cGANs with and without Classifiers Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin
Online and Offline Reinforcement Learning by Planning with a Learned Model Julian Schrittwieser, Thomas Hubert, Amol Mandhane, Mohammadamin Barekatain, Ioannis Antonoglou, David Silver
Stochastic Multi-Armed Bandits with Control Variates Arun Verma, Manjesh Kumar Hanawal
Near-Optimal No-Regret Learning in General Games Constantinos Daskalakis, Maxwell Fishelson, Noah Golowich
Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration Yu Wang, Jingyang Lin, Jingjing Zou, Yingwei Pan, Ting Yao, Tao Mei
Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss Michael Iuzzolino, Michael Mozer, Samy Bengio
Identifiable Generative models for Missing Not at Random Data Imputation Chao Ma, Cheng Zhang
DNN-based Topology Optimisation: Spatial Invariance and Neural Tangent Kernel Benjamin Dupuis, Arthur Jacot
Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval Omar Khattab, Christopher Potts, Matei Zaharia
Local Hyper-Flow Diffusion Kimon Fountoulakis, Pan Li, Shenghao Yang
Permuton-induced Chinese Restaurant Process Masahiro Nakano, Yasuhiro Fujiwara, Akisato Kimura, Takeshi Yamada, naonori ueda
Faster Algorithms and Constant Lower Bounds for the Worst-Case Expected Error Jonah Brown-Cohen
On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources Trung Phung, Trung Le, Tung-Long Vuong, Toan Tran, Anh Tran, Hung Bui, Dinh Phung
You Never Cluster Alone Yuming Shen, Ziyi Shen, Menghan Wang, Jie Qin, Philip Torr, Ling Shao
Dynamic COVID risk assessment accounting for community virus exposure from a spatial-temporal transmission model Yuan Chen, Wenbo Fei, Qinxia Wang, Donglin Zeng, Yuanjia Wang
Dueling Bandits with Adversarial Sleeping Aadirupa Saha, Pierre Gaillard
Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy to Game Alexander Reisach, Christof Seiler, Sebastian Weichwald
Automated Dynamic Mechanism Design Hanrui Zhang, Vincent Conitzer
A generative nonparametric Bayesian model for whole genomes Alan Amin, Eli N Weinstein, Debora Marks
Robust Predictable Control Ben Eysenbach, Ruslan Salakhutdinov, Sergey Levine
Unsupervised Speech Recognition Alexei Baevski, Wei-Ning Hsu, Alexis CONNEAU, Michael Auli
Robustness between the worst and average case Leslie Rice, Anna Bair, Huan Zhang, J. Zico Kolter
Online Learning and Control of Complex Dynamical Systems from Sensory Input Oumayma Bounou, Jean Ponce, Justin Carpentier
Self-Supervised Bug Detection and Repair Miltiadis Allamanis, Henry Jackson-Flux, Marc Brockschmidt
Faster Neural Network Training with Approximate Tensor Operations Menachem Adelman, Kfir Levy, Ido Hakimi, Mark Silberstein
Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach Fan Yang, Kai He, Linxiao Yang, Hongxia Du, Jingbang Yang, Bo Yang, Liang Sun
Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis Yutong He, Dingjie Wang, Nicholas Lai, William Zhang, Chenlin Meng, Marshall Burke, David Lobell, Stefano Ermon
On Memorization in Probabilistic Deep Generative Models Gerrit van den Burg, Chris Williams
You Are the Best Reviewer of Your Own Papers: An Owner-Assisted Scoring Mechanism Weijie Su
Garment4D: Garment Reconstruction from Point Cloud Sequences Fangzhou Hong, Liang Pan, Zhongang Cai, Ziwei Liu
Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization Shicong Cen, Yuting Wei, Yuejie Chi
Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data Qi Zhu, Natalia Ponomareva, Jiawei Han, Bryan Perozzi
RIM: Reliable Influence-based Active Learning on Graphs Wentao Zhang, Yexin Wang, Zhenbang You, Meng Cao, Ping Huang, Jiulong Shan, Zhi Yang, Bin CUI
Dynamical Wasserstein Barycenters for Time-series Modeling Kevin Cheng, Shuchin Aeron, Michael C Hughes, Eric L Miller
RelaySum for Decentralized Deep Learning on Heterogeneous Data Thijs Vogels, Lie He, Anastasiia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U Stich, Martin Jaggi
Transformers Generalize DeepSets and Can be Extended to Graphs & Hypergraphs Jinwoo Kim, Saeyoon Oh, Seunghoon Hong
No Regrets for Learning the Prior in Bandits Soumya Basu, Branislav Kveton, Manzil Zaheer, Csaba Szepesvari
Encoding Robustness to Image Style via Adversarial Feature Perturbations Manli Shu, Zuxuan Wu, Micah Goldblum, Tom Goldstein
Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms Mathieu Even, Raphaël Berthier, Francis R. Bach, Nicolas Flammarion, Hadrien Hendrikx, Pierre Gaillard, Laurent Massoulié, Adrien Taylor
Natural continual learning: success is a journey, not (just) a destination Ta-Chu Kao, Kristopher Jensen, Gido van de Ven, Alberto Bernacchia, Guillaume Hennequin
Individual Privacy Accounting via a Rényi Filter Vitaly Feldman, Tijana Zrnic
Post-Training Quantization for Vision Transformer Zhenhua Liu, Yunhe Wang, Kai Han, Wei Zhang, Siwei Ma, Wen Gao
Unsupervised Part Discovery from Contrastive Reconstruction Subhabrata Choudhury, Iro Laina, Christian Rupprecht, Andrea Vedaldi
ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning Guocheng Qian, Hasan Hammoud, Guohao Li, Ali Thabet, Bernard Ghanem
An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers Ramakrishna Vedantam, David Lopez-Paz, David J Schwab
Fair Sequential Selection Using Supervised Learning Models Mohammad Mahdi Khalili, Xueru Zhang, Mahed Abroshan
Towards Sample-efficient Overparameterized Meta-learning Yue Sun, Adhyyan Narang, Ibrahim Gulluk, Samet Oymak, Maryam Fazel
ScaleCert: Scalable Certified Defense against Adversarial Patches with Sparse Superficial Layers Husheng Han, Kaidi Xu, Xing Hu, Xiaobing Chen, Ling LIANG, Zidong Du, Qi Guo, Yanzhi Wang, Yunji Chen
Towards mental time travel: a hierarchical memory for reinforcement learning agents Andrew Lampinen, Stephanie Chan, Andrea Banino, Felix Hill
Beyond Tikhonov: faster learning with self-concordant losses, via iterative regularization Gaspard Beugnot, Julien Mairal, Alessandro Rudi
Variational Bayesian Reinforcement Learning with Regret Bounds Brendan O'Donoghue
Logarithmic Regret from Sublinear Hints Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
Independent mechanism analysis, a new concept? Luigi Gresele, Julius von Kügelgen, Vincent Stimper, Bernhard Schölkopf, Michel Besserve
Momentum Centering and Asynchronous Update for Adaptive Gradient Methods Juntang Zhuang, Yifan Ding, Tommy Tang, Nicha Dvornek, Sekhar C Tatikonda, James Duncan
Robustness via Uncertainty-aware Cycle Consistency Uddeshya Upadhyay, Yanbei Chen, Zeynep Akata
CBP: backpropagation with constraint on weight precision using a pseudo-Lagrange multiplier method Guhyun Kim, Doo Seok Jeong
On the Sample Complexity of Privately Learning Axis-Aligned Rectangles Menachem Sadigurschi, Uri Stemmer
Implicit Sparse Regularization: The Impact of Depth and Early Stopping Jiangyuan Li, Thanh Nguyen, Chinmay Hegde, Ka Wai Wong
Efficient Generalization with Distributionally Robust Learning Soumyadip Ghosh, Mark Squillante, Ebisa Wollega
No-regret Online Learning over Riemannian Manifolds Xi Wang, Zhipeng Tu, Yiguang Hong, Yingyi Wu, Guodong Shi
Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning Junsu Kim, Younggyo Seo, Jinwoo Shin
Minimax Regret for Stochastic Shortest Path Alon Cohen, Yonathan Efroni, Yishay Mansour, Aviv Rosenberg
Parametrized Quantum Policies for Reinforcement Learning Sofiene Jerbi, Casper Gyurik, Simon Marshall, Hans Briegel, Vedran Dunjko
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations Tim G. J. Rudner, Cong Lu, Michael A Osborne, Yarin Gal, Yee W. Teh
Conditional Generation Using Polynomial Expansions Grigorios Chrysos, Markos Georgopoulos, Yannis Panagakis
Efficient constrained sampling via the mirror-Langevin algorithm Kwangjun Ahn, Sinho Chewi
Adaptive Online Packing-guided Search for POMDPs Chenyang Wu, Guoyu Yang, Zongzhang Zhang, Yang Yu, Dong Li, Wulong Liu, Jianye Hao
Turing Completeness of Bounded-Precision Recurrent Neural Networks Stephen Chung, Hava T. Siegelmann
End-to-end Multi-modal Video Temporal Grounding Yi-Wen Chen, Yi-Hsuan Tsai, Ming-Hsuan Yang
How Powerful are Performance Predictors in Neural Architecture Search? Colin White, Arber Zela, Robin Ru, Yang Liu, Frank Hutter
Stylized Dialogue Generation with Multi-Pass Dual Learning Jinpeng Li, Yingce Xia, Rui Yan, Hongda Sun, Dongyan Zhao, Tie-Yan Liu
Entropy-based adaptive Hamiltonian Monte Carlo Marcel Hirt, Michalis K. Titsias, Petros Dellaportas
Continual World: A Robotic Benchmark For Continual Reinforcement Learning Maciej Wołczyk, Michał Zając, Razvan Pascanu, Łukasz Kuciński, Piotr Miłoś
Towards Best-of-All-Worlds Online Learning with Feedback Graphs Liad Erez, Tomer Koren
ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias Yufei Xu, Qiming ZHANG, Jing Zhang, Dacheng Tao
Open Rule Induction Wanyun Cui, Xingran Chen
Post-Contextual-Bandit Inference Aurelien Bibaut, Maria Dimakopoulou, Nathan Kallus, Antoine Chambaz, Mark van der Laan
Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme Shaojie Li, Jie Wu, Xuefeng Xiao, Fei Chao, Xudong Mao, Rongrong Ji
Asymptotically Exact Error Characterization of Offline Policy Evaluation with Misspecified Linear Models Kohei Miyaguchi
Topographic VAEs learn Equivariant Capsules T. Anderson Keller, Max Welling
MobILE: Model-Based Imitation Learning From Observation Alone Rahul Kidambi, Jonathan Chang, Wen Sun
Few-Round Learning for Federated Learning Younghyun Park, Dong-Jun Han, Do-Yeon Kim, Jun Seo, Jaekyun Moon
On Path Integration of Grid Cells: Group Representation and Isotropic Scaling Ruiqi Gao, Jianwen Xie, Xue-Xin Wei, Song-Chun Zhu, Ying Nian Wu
Online Convex Optimization with Continuous Switching Constraint Guanghui Wang, Yuanyu Wan, Tianbao Yang, Lijun Zhang
Why Do Better Loss Functions Lead to Less Transferable Features? Simon Kornblith, Ting Chen, Honglak Lee, Mohammad Norouzi
Breaking the centralized barrier for cross-device federated learning Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian U Stich, Ananda Theertha Suresh
Adversarially robust learning for security-constrained optimal power flow Priya Donti, Aayushya Agarwal, Neeraj Vijay Bedmutha, Larry Pileggi, J. Zico Kolter
Learning a Single Neuron with Bias Using Gradient Descent Gal Vardi, Gilad Yehudai, Ohad Shamir
Making a (Counterfactual) Difference One Rationale at a Time Mitchell Plyler, Michael Green, Min Chi
3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds Le Hui, Lingpeng Wang, Mingmei Cheng, Jin Xie, Jian Yang
Stateful Strategic Regression Keegan Harris, Hoda Heidari, Steven Z. Wu
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning Jannik Kossen, Neil Band, Clare Lyle, Aidan N Gomez, Thomas Rainforth, Yarin Gal
Your head is there to move you around: Goal-driven models of the primate dorsal pathway Patrick Mineault, Shahab Bakhtiari, Blake Richards, Christopher Pack
Achieving Rotational Invariance with Bessel-Convolutional Neural Networks Valentin Delchevalerie, Adrien Bibal, Benoît Frénay, Alexandre Mayer
Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning Jinxin Liu, Hao Shen, Donglin Wang, Yachen Kang, Qiangxing Tian
GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph Junhan Yang, Zheng Liu, Shitao Xiao, Chaozhuo Li, Defu Lian, Sanjay Agrawal, Amit Singh, Guangzhong Sun, Xing Xie
A Universal Law of Robustness via Isoperimetry Sebastien Bubeck, Mark Sellke
On Contrastive Representations of Stochastic Processes Emile Mathieu, Adam Foster, Yee W. Teh
A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance Sudeep Salgia, Sattar Vakili, Qing Zhao
Scalars are universal: Equivariant machine learning, structured like classical physics Soledad Villar, David W Hogg, Kate Storey-Fisher, Weichi Yao, Ben Blum-Smith
Unsupervised Object-Level Representation Learning from Scene Images Jiahao Xie, Xiaohang Zhan, Ziwei Liu, Yew Soon Ong, Chen Change Loy
Do Transformers Really Perform Badly for Graph Representation? Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu
Powerpropagation: A sparsity inducing weight reparameterisation Jonathan Schwarz, Siddhant Jayakumar, Razvan Pascanu, Peter E Latham, Yee W. Teh
Stronger NAS with Weaker Predictors Junru Wu, Xiyang Dai, Dongdong Chen, Yinpeng Chen, Mengchen Liu, Ye Yu, Zhangyang Wang, Zicheng Liu, Mei Chen, Lu Yuan
Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training Sheng Liu, Xiao Li, Yuexiang Zhai, Chong You, Zhihui Zhu, Carlos Fernandez-Granda, Qing Qu
Nearly-Tight and Oblivious Algorithms for Explainable Clustering Buddhima Gamlath, Xinrui Jia, Adam Polak, Ola Svensson
Deep Networks Provably Classify Data on Curves Tingran Wang, Sam Buchanan, Dar Gilboa, John Wright
COMBO: Conservative Offline Model-Based Policy Optimization Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn
Time-series Generation by Contrastive Imitation Daniel Jarrett, Ioana Bica, Mihaela van der Schaar
Differentially Private Sampling from Distributions Sofya Raskhodnikova, Satchit Sivakumar, Adam Smith, Marika Swanberg
On the Expected Complexity of Maxout Networks Hanna Tseran, Guido F. Montufar
Cross-view Geo-localization with Layer-to-Layer Transformer Hongji Yang, Xiufan Lu, Yingying Zhu
TAAC: Temporally Abstract Actor-Critic for Continuous Control Haonan Yu, Wei Xu, Haichao Zhang
Learning Robust Hierarchical Patterns of Human Brain across Many fMRI Studies Dushyant Sahoo, Christos Davatzikos
Global Convergence to Local Minmax Equilibrium in Classes of Nonconvex Zero-Sum Games Tanner Fiez, Lillian Ratliff, Eric Mazumdar, Evan Faulkner, Adhyyan Narang
Bandit Quickest Changepoint Detection Aditya Gopalan, Braghadeesh Lakshminarayanan, Venkatesh Saligrama
Can multi-label classification networks know what they don’t know? Haoran Wang, Weitang Liu, Alex Bocchieri, Yixuan Li
Balanced Chamfer Distance as a Comprehensive Metric for Point Cloud Completion Tong Wu, Liang Pan, Junzhe Zhang, Tai WANG, Ziwei Liu, Dahua Lin
Optimal Gradient-based Algorithms for Non-concave Bandit Optimization Baihe Huang, Kaixuan Huang, Sham Kakade, Jason Lee, Qi Lei, Runzhe Wang, Jiaqi Yang
On Optimal Interpolation in Linear Regression Eduard Oravkin, Patrick Rebeschini
Differentiable Optimization of Generalized Nondecomposable Functions using Linear Programs Zihang Meng, Lopamudra Mukherjee, Yichao Wu, Vikas Singh, Sathya Ravi
Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization Jianhao Wang, Zhizhou Ren, Beining Han, Jianing Ye, Chongjie Zhang
Margin-Independent Online Multiclass Learning via Convex Geometry Guru Guruganesh, Allen Liu, Jon Schneider, Joshua Wang
STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data Zhi Zhou, Lan-Zhe Guo, Zhanzhan Cheng, Yu-Feng Li, Shiliang Pu
Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning Antonious Girgis, Deepesh Data, Suhas Diggavi
Gradient-based Editing of Memory Examples for Online Task-free Continual Learning Xisen Jin, Arka Sadhu, Junyi Du, Xiang Ren
Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time Ferran Alet, Maria Bauza, Kenji Kawaguchi, Nurullah Giray Kuru, Tomás Lozano-Pérez, Leslie P. Kaelbling
Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity Scott Pesme, Loucas Pillaud-Vivien, Nicolas Flammarion
Iterative Teacher-Aware Learning Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu
Clockwork Variational Autoencoders Vaibhav Saxena, Jimmy Ba, Danijar Hafner
How Does it Sound? Kun Su, Xiulong Liu, Eli Shlizerman
Stabilizing Dynamical Systems via Policy Gradient Methods Juan Perdomo, Jack Umenberger, Max Simchowitz
Language models enable zero-shot prediction of the effects of mutations on protein function Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu, Alex Rives
Deep Reinforcement Learning at the Edge of the Statistical Precipice Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc Bellemare
DRONE: Data-aware Low-rank Compression for Large NLP Models Patrick Chen, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh
DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning Hussein Hazimeh, Zhe Zhao, Aakanksha Chowdhery, Maheswaran Sathiamoorthy, Yihua Chen, Rahul Mazumder, Lichan Hong, Ed Chi
Mind the Gap: Assessing Temporal Generalization in Neural Language Models Angeliki Lazaridou, Adhi Kuncoro, Elena Gribovskaya, Devang Agrawal, Adam Liska, Tayfun Terzi, Mai Gimenez, Cyprien de Masson d'Autume, Tomas Kocisky, Sebastian Ruder, Dani Yogatama, Kris Cao, Susannah Young, Phil Blunsom
Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks Melih Barsbey, Milad Sefidgaran, Murat A Erdogdu, Gaël Richard, Umut Simsekli
Targeted Neural Dynamical Modeling Cole Hurwitz, Akash Srivastava, Kai Xu, Justin Jude, Matthew Perich, Lee Miller, Matthias H. Hennig
Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation Shiqi Yang, yaxing wang, Joost van de Weijer, Luis Herranz, Shangling Jui
Learning with Noisy Correspondence for Cross-modal Matching Zhenyu Huang, Guocheng Niu, Xiao Liu, Wenbiao Ding, Xinyan Xiao, Hua Wu, Xi Peng
Offline Reinforcement Learning with Reverse Model-based Imagination Jianhao Wang, Wenzhe Li, Haozhe Jiang, Guangxiang Zhu, Siyuan Li, Chongjie Zhang
Parameter Prediction for Unseen Deep Architectures Boris Knyazev, Michal Drozdzal, Graham W. Taylor, Adriana Romero Soriano
FMMformer: Efficient and Flexible Transformer via Decomposed Near-field and Far-field Attention Tan Nguyen, Vai Suliafu, Stanley Osher, Long Chen, Bao Wang
Square Root Principal Component Pursuit: Tuning-Free Noisy Robust Matrix Recovery Junhui Zhang, Jingkai Yan, John Wright
Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal Xhonneux, Jian Tang
CorticalFlow: A Diffeomorphic Mesh Transformer Network for Cortical Surface Reconstruction Leo Lebrat, Rodrigo Santa Cruz, Frederic de Gournay, Darren Fu, Pierrick Bourgeat, Jurgen Fripp, Clinton Fookes, Olivier Salvado
Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning Nan Ding, Xi Chen, Tomer Levinboim, Sebastian Goodman, Radu Soricut
SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression Steve Yadlowsky, Taedong Yun, Cory Y McLean, Alexander D'Amour
ELLA: Exploration through Learned Language Abstraction Suvir Mirchandani, Siddharth Karamcheti, Dorsa Sadigh
Learning Distilled Collaboration Graph for Multi-Agent Perception Yiming Li, Shunli Ren, Pengxiang Wu, Siheng Chen, Chen Feng, Wenjun Zhang
Federated-EM with heterogeneity mitigation and variance reduction Aymeric Dieuleveut, Gersende Fort, Eric Moulines, Geneviève Robin
On the Role of Optimization in Double Descent: A Least Squares Study Ilja Kuzborskij, Csaba Szepesvari, Omar Rivasplata, Amal Rannen-Triki, Razvan Pascanu
Neural Architecture Dilation for Adversarial Robustness Yanxi Li, Zhaohui Yang, Yunhe Wang, Chang Xu
Clustering Effect of Adversarial Robust Models Yang Bai, Xin Yan, Yong Jiang, Shu-Tao Xia, Yisen Wang
On the Cryptographic Hardness of Learning Single Periodic Neurons Min Jae Song, Ilias Zadik, Joan Bruna
PCA Initialization for Approximate Message Passing in Rotationally Invariant Models Marco Mondelli, Ramji Venkataramanan
Automatic and Harmless Regularization with Constrained and Lexicographic Optimization: A Dynamic Barrier Approach Chengyue Gong, Xingchao Liu, Qiang Liu
Corruption Robust Active Learning Yifang Chen, Simon S Du, Kevin G. Jamieson
Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models Runzhe Wan, Lin Ge, Rui Song
Program Synthesis Guided Reinforcement Learning for Partially Observed Environments Yichen Yang, Jeevana Priya Inala, Osbert Bastani, Yewen Pu, Armando Solar-Lezama, Martin Rinard
Robust Allocations with Diversity Constraints Zeyu Shen, Lodewijk Gelauff, Ashish Goel, Aleksandra Korolova, Kamesh Munagala
Activation Sharing with Asymmetric Paths Solves Weight Transport Problem without Bidirectional Connection Sunghyeon Woo, Jeongwoo Park, Jiwoo Hong, Dongsuk Jeon
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation Mingcong Liu, Qiang Li, Zekui Qin, Guoxin Zhang, Pengfei Wan, Wen Zheng
Differentially Private Model Personalization Prateek Jain, John Rush, Adam Smith, Shuang Song, Abhradeep Guha Thakurta
Rates of Estimation of Optimal Transport Maps using Plug-in Estimators via Barycentric Projections NABARUN DEB, Promit Ghosal, Bodhisattva Sen
Robust Generalization despite Distribution Shift via Minimum Discriminating Information Tobias Sutter, Andreas Krause, Daniel Huhn
Soft Calibration Objectives for Neural Networks Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael Mozer, Becca Roelofs
Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models Lenart Treven, Philippe Wenk, Florian Dorfler, Andreas Krause
Shaping embodied agent behavior with activity-context priors from egocentric video Tushar Nagarajan, Kristen Grauman
Adjusting for Autocorrelated Errors in Neural Networks for Time Series Fan-Keng Sun, Chris Lang, Duane Boning
A Geometric Analysis of Neural Collapse with Unconstrained Features Zhihui Zhu, Tianyu Ding, Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, Qing Qu
NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wild Jason Zhang, Gengshan Yang, Shubham Tulsiani, Deva Ramanan
Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning Yifan Zhang, Bryan Hooi, Dapeng Hu, Jian Liang, Jiashi Feng
Discovery of Options via Meta-Learned Subgoals Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhongwen Xu, Junhyuk Oh, Iurii Kemaev, Hado P van Hasselt, David Silver, Satinder P. Singh
Near-Optimal Lower Bounds For Convex Optimization For All Orders of Smoothness Ankit Garg, Robin Kothari, Praneeth Netrapalli, Suhail Sherif
Topology-Imbalance Learning for Semi-Supervised Node Classification Deli Chen, Yankai Lin, Guangxiang Zhao, Xuancheng Ren, Peng Li, Jie Zhou, Xu Sun
Gradient Inversion with Generative Image Prior Jinwoo Jeon, jaechang Kim, Kangwook Lee, Sewoong Oh, Jungseul Ok
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Robustness Verification Shiqi Wang, Huan Zhang, Kaidi Xu, Xue Lin, Suman Jana, Cho-Jui Hsieh, J. Zico Kolter
Autobahn: Automorphism-based Graph Neural Nets Erik Thiede, Wenda Zhou, Risi Kondor
Data Augmentation Can Improve Robustness Sylvestre-Alvise Rebuffi, Sven Gowal, Dan Andrei Calian, Florian Stimberg, Olivia Wiles, Timothy A Mann
Deep Explicit Duration Switching Models for Time Series Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle, Ali Caner Turkmen, Harold Soh, Alexander J Smola, Bernie Wang, Tim Januschowski
Shared Independent Component Analysis for Multi-Subject Neuroimaging Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvarinen
Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, Marc Pollefeys
Batched Thompson Sampling Cem Kalkanli, Ayfer Ozgur
Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning Ligeng Zhu, Hongzhou Lin, Yao Lu, Yujun Lin, Song Han
Focal Attention for Long-Range Interactions in Vision Transformers Jianwei Yang, Chunyuan Li, Pengchuan Zhang, Xiyang Dai, Bin Xiao, Lu Yuan, Jianfeng Gao
Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints Henry C Bendekgey, Erik B. Sudderth
Residual Pathway Priors for Soft Equivariance Constraints Marc Finzi, Gregory Benton, Andrew G Wilson
Optimal Algorithms for Stochastic Contextual Preference Bandits Aadirupa Saha
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Kevin Scaman, Hoi-To Wai
Learning Large Neighborhood Search Policy for Integer Programming Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang
Dynamic Trace Estimation Prathamesh Dharangutte, Christopher Musco
Provable Representation Learning for Imitation with Contrastive Fourier Features Ofir Nachum, Mengjiao Yang
MICo: Improved representations via sampling-based state similarity for Markov decision processes Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland
Counterfactual Explanations in Sequential Decision Making Under Uncertainty Stratis Tsirtsis, Abir De, Manuel Rodriguez
Streaming Linear System Identification with Reverse Experience Replay Suhas Kowshik, Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli
SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness Jongheon Jeong, Sejun Park, Minkyu Kim, Heung-Chang Lee, Do-Guk Kim, Jinwoo Shin
Action-guided 3D Human Motion Prediction Jiangxin Sun, Zihang Lin, Xintong Han, Jian-Fang Hu, Jia Xu, Wei-Shi Zheng
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks Maksym Yatsura, Jan Metzen, Matthias Hein
Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory Zeru Zhang, Jiayin Jin, Zijie Zhang, Yang Zhou, Xin Zhao, Jiaxiang Ren, Ji Liu, Lingfei Wu, Ruoming Jin, Dejing Dou
Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training Shangshu Qian, Viet Hung Pham, Thibaud Lutellier, Zeou Hu, Jungwon Kim, Lin Tan, Yaoliang Yu, Jiahao Chen, Sameena Shah
Rectangular Flows for Manifold Learning Anthony L Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
On the Generative Utility of Cyclic Conditionals Chang Liu, Haoyue Tang, Tao Qin, Jintao Wang, Tie-Yan Liu
Structural Credit Assignment in Neural Networks using Reinforcement Learning Dhawal Gupta, Gabor Mihucz, Matthew Schlegel, James Kostas, Philip S. Thomas, Martha White
A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum Prashant Khanduri, Siliang Zeng, Mingyi Hong, Hoi-To Wai, Zhaoran Wang, Zhuoran Yang
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels Erik Englesson, Hossein Azizpour
Continual Learning via Local Module Composition Oleksiy Ostapenko, Pau Rodriguez, Massimo Caccia, Laurent Charlin
Model-Based Episodic Memory Induces Dynamic Hybrid Controls Hung Le, Thommen Karimpanal George, Majid Abdolshah, Truyen Tran, Svetha Venkatesh
FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization Quoc Tran Dinh, Nhan H Pham, Dzung Phan, Lam Nguyen
Adversarial Examples Make Strong Poisons Liam Fowl, Micah Goldblum, Ping-yeh Chiang, Jonas Geiping, Wojciech Czaja, Tom Goldstein
Coresets for Decision Trees of Signals Ibrahim Jubran, Ernesto Evgeniy Sanches Shayda, Ilan I Newman, Dan Feldman
Local plasticity rules can learn deep representations using self-supervised contrastive predictions Bernd Illing, Jean Ventura, Guillaume Bellec, Wulfram Gerstner
MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction Hao Xue, Flora Salim, Yongli Ren, Nuria Oliver
Early Convolutions Help Transformers See Better Tete Xiao, Mannat Singh, Eric Mintun, Trevor Darrell, Piotr Dollar, Ross B. Girshick
Error Compensated Distributed SGD Can Be Accelerated Xun Qian, Peter Richtarik, Tong Zhang
InfoGCL: Information-Aware Graph Contrastive Learning Dongkuan Xu, Wei Cheng, Dongsheng Luo, Haifeng Chen, Xiang Zhang
Meta-Learning for Relative Density-Ratio Estimation Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning Vivien Cabannes, Loucas Pillaud-Vivien, Francis R. Bach, Alessandro Rudi
Unlabeled Principal Component Analysis Yunzhen Yao, Liangzu Peng, Manolis Tsakiris
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal
Scalable Rule-Based Representation Learning for Interpretable Classification Zhuo Wang, Wei Zhang, Ning Liu, Jianyong Wang
Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection NA DONG, Yongqiang Zhang, Mingli Ding, Gim Hee Lee
A Regression Approach to Learning-Augmented Online Algorithms Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi

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