Deep Structured Mixtures of Gaussian Processes

Martin Trapp, Robert Peharz, Franz Pernkopf, Carl E. Rasmussen

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

16 Citations (Scopus)


Gaussian Processes (GPs) are powerful non-parametric Bayesian regression models that allow exact posterior inference, but exhibit high computational and memory costs. In order to improve scalability of GPs, approximate posterior inference is frequently employed, where a prominent class of approximation techniques is based on local GP experts. However, local-expert techniques proposed so far are either not well-principled, come with limited approximation guarantees, or lead to intractable models. In this paper, we introduce deep structured mixtures of GP experts, a stochastic process model which i) allows exact posterior inference, ii) has attractive computational and memory costs, and iii) when used as GP approximation, captures predictive uncertainties consistently better than previous expert-based approximations. In a variety of experiments, we show that deep structured mixtures have a low approximation error and often perform competitive or outperform prior work.
Original languageEnglish
Title of host publicationProceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (PMLR)
EditorsSilvia Chiappa, Roberta Calandra
Number of pages11
Publication statusPublished - 26 Apr 2020
Event23rd International Conference on Artificial Intelligence and Statistics, ONLINE - Palermo, Italy
Duration: 26 Aug 202028 Aug 2020

Publication series

NameProceedings of Machine Learning Research (PMLR)
ISSN (Print)2640-3498


Conference23rd International Conference on Artificial Intelligence and Statistics, ONLINE
Abbreviated titleAISTATS 2020


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