Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression

Part of Advances in Neural Information Processing Systems 25 (NIPS 2012)

Authors

Mohammad Emtiyaz Khan, Shakir Mohamed, Kevin P. Murphy

Abstract

We present a new variational inference algorithm for Gaussian processes with non-conjugate likelihood functions. This includes binary and multi-class classification, as well as ordinal regression. Our method constructs a convex lower bound, which can be optimized by using an efficient fixed point update method. We then show empirically that our new approach is much faster than existing methods without any degradation in performance.