Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation

Code

• github.com/theogf/AugmentedGaussianProcesses.jl
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• github.com/UnofficialJuliaMirrorSnapshots/AugmentedGaussianProcesses.jl-38eea1fd-7d7d-5162-9d08-f89d0f2e271e
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• github.com/UnofficialJuliaMirror/AugmentedGaussianProcesses.jl-38eea1fd-7d7d-5162-9d08-f89d0f2e271e
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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.

Tasks

Reproductions