Gaussian Processes with Bayesian Inference of Covariate Couplings
Abstract: Gaussian processes are powerful probabilistic models that are often coupled with Automatic Relevance Determination (ARD) capable of uncovering the importance of individual covariates. We develop covariances characterized by affine transformations of the inputs, formalized via a precision matrix between covariates, which can uncover covariate couplings for enhanced interpretability. We study a range of couplings priors from Wishart to Horseshoe and present fully Bayesian inference of such precision matrices within sparse Gaussian process. We demonstrate empirically the efficacy and interpretability of this approach.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Arto_Klami1
Submission Number: 3231
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