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  • 2009.12141v1

    Final published version, 1.79 MB, PDF document

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Stein Variational Gaussian Processes

Research output: Contribution to journalJournal articlepeer-review

Published
<mark>Journal publication date</mark>25/09/2020
<mark>Journal</mark>arXiv
Publication StatusPublished
<mark>Original language</mark>English

Abstract

We show how to use Stein variational gradient descent (SVGD) to carry out inference in Gaussian process (GP) models with non-Gaussian likelihoods and large data volumes. Markov chain Monte Carlo (MCMC) is extremely computationally intensive for these situations, but the parametric assumptions required for efficient variational inference (VI) result in incorrect inference when they encounter the multi-modal posterior distributions that are common for such models. SVGD provides a non-parametric alternative to variational inference which is substantially faster than MCMC but unhindered by parametric assumptions. We prove that for GP models with Lipschitz gradients the SVGD algorithm monotonically decreases the Kullback-Leibler divergence from the sampling distribution to the true posterior. Our method is demonstrated on benchmark problems in both regression and classification, and a real air quality example with 11440 spatiotemporal observations, showing substantial performance improvements over MCMC and VI.

Bibliographic note

25 pages, 5 figures