Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation

We propose a scalable stochastic variational approach to GP classification building on Pólya-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.

Authors

• Florian Wenzel
• Théo Galy-Fajou
• Christan Donner
• Marius Kloft Technische Universität Kaiserslautern
• Manfred Opper

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