Scalable Logit Gaussian Process Classification

Abstract

We propose an efficient stochastic variational approach to Gaussian Process (GP) classification building on Pólya-Gamma data augmentation and inducing points, which is based on closed-form updates of natural gradients. We evaluate the algorithm on real-world datasets containing up to 11 million data points and demonstrate that it is up to two orders of magnitude faster than the state-of-the-art while being competitive in terms of prediction performance.

Publication
NIPS 2017 Workshop on Advances in Approximate Bayesian Inference
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Netfilx Travel Award Oral Presentation
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