Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation
Machine Learning
2018-11-28 v2 Machine Learning
Abstract
We propose a scalable stochastic variational approach to GP classification building on Polya-Gamma data augmentation and inducing points. Unlike former approaches, we obtain closed-form updates based on natural gradients that lead to efficient optimization. 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.
Cite
@article{arxiv.1802.06383,
title = {Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation},
author = {Florian Wenzel and Theo Galy-Fajou and Christan Donner and Marius Kloft and Manfred Opper},
journal= {arXiv preprint arXiv:1802.06383},
year = {2018}
}