Query-aware bayesian committee machine for scalable gaussian process regression

Abstract

Copyright © 2019 by SIAM. The Gaussian process (GP) model is a powerful tool for regression problems. However, the high computational costs of the GP model has constrained its applications over large-scale data sets. To overcome this limitation, aggregation models employ distributed GP submodels (experts) for parallel training and predicting, and then merge the predictions of all submodels to produce an approximated result. The state-of-the-art aggregation models are based on Bayesian committee machines, where a prior is assumed at the start and then updated by each submodel. In this paper, we investigate the impact of the prior on the accuracy of aggregations. We propose a query-aware Bayes..