Stein Variational Gaussian Processes

Pinder, Thomas and Nemeth, Christopher and Leslie, David (2020) Stein Variational Gaussian Processes. arXiv.

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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.

Item Type:
Journal Article
Journal or Publication Title:
arXiv
Additional Information:
25 pages, 5 figures
Subjects:
ID Code:
148463
Deposited By:
Deposited On:
23 Oct 2020 15:00
Refereed?:
Yes
Published?:
Published
Last Modified:
29 Nov 2020 07:05