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arXiv · 1802.06383

Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation

Florian Wenzel·Theo Galy-Fajou·Christan Donner·Marius Kloft·Manfred Opper
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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.

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Florian Wenzel, Theo Galy-Fajou, Christan Donner, Marius Kloft, Manfred Opper. 2018-02-18. Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation. https://arxiv.org/abs/1802.06383

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