GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models

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Pavel Berkovich, Eric Perim, Wessel Bruinsma ;
Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference, PMLR 118:1-14, 2020.

Abstract

In this work, we apply Bayesian model selection to the calibration of the complexity of the latent space. We propose an extension of the LMM that automatically chooses the latent processes by turning off those that do not meaningfully contribute to explaining the data. We call the technique Gaussian Process Automatic Latent Process Selection (GPALPS). The extra functionality of GP-ALPS comes at the cost of exact inference, so we devise a variational inference (VI) scheme and demonstrate its suitability in a set of preliminary experiments. We also assess the quality of the variational posterior by comparing our approximate results with those obtained via a Markov Chain Monte Carlo (MCMC) approach.

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