Dance, Hugh; Paige, Brooks; (2022) Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes. In: Camps-Valls, G and Ruiz, FJR and Valera, I, (eds.) Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022. (pp. pp. 1-27). PMLR

Preview

Text dance22a.pdf - Published Version Download (1MB) | Preview

Abstract

Variable selection in Gaussian processes (GPs) is typically undertaken by thresholding the inverse lengthscales of automatic relevance determination kernels, but in high-dimensional datasets this approach can be unreliable. A more probabilistically principled alternative is to use spike and slab priors and infer a posterior probability of variable inclusion. However, existing implementations in GPs are very costly to run in both high-dimensional and large-n datasets, or are only suitable for unsupervised settings with specific kernels. As such, we develop a fast and scalable variational inference algorithm for the spike and slab GP that is tractable with arbitrary differentiable kernels. We improve our algorithm’s ability to adapt to the sparsity of relevant variables by Bayesian model averaging over hyperparameters, and achieve substantial speed ups using zero temperature posterior restrictions, dropout pruning and nearest neighbour minibatching. In experiments our method consistently outperforms vanilla and sparse variational GPs whilst retaining similar runtimes (even when n=10^6) and performs competitively with a spike and slab GP using MCMC but runs up to 1000 times faster.

Download activity - last month

Export as XMLCSVJSON

Download activity - last 12 months

Export as JSONCSVXML

Downloads by country - last 12 months

Export as XMLCSVJSON

Archive Staff Only

View Item