Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regression

Zhongjie Yu*, Mingye Zhu, Martin Trapp, Arseny Skryagin, Kristian Kersting

*Corresponding author for this work

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

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Abstract

Inspired by recent advances in the field of expert-based approximations of Gaussian processes (GPs), we present an expert-based approach to large-scale
multi-output regression using single-output GP experts. Employing a deeply structured mixture of single-output GPs encoded via a probabilistic circuit allows us to capture correlations between multiple output dimensions accurately. By recursively partitioning the covariate space and the output space, posterior inference in our model reduces to inference on single-output GP experts, which only need to be conditioned on a small subset of the observations. We show that inference can be performed exactly and efficiently in our model, that it can capture correlations between output dimensions and, hence, often outperforms approaches that do not incorporate inter-output correlations, as demonstrated on several data sets in terms of the negative log predictive density.
Original languageEnglish
Title of host publicationProceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence
Pages2008-2018
Number of pages11
Publication statusPublished - 2021
MoE publication typeA4 Article in a conference publication
EventConference on Uncertainty in Artificial Intelligence - Virtual, Online
Duration: 27 Jul 202129 Jul 2021
https://auai.org/uai2021/

Publication series

NameProceedings of Machine Learning Research
PublisherPMLR
Volume161
ISSN (Electronic)2640-3498

Conference

ConferenceConference on Uncertainty in Artificial Intelligence
Abbreviated titleUAI
CityVirtual, Online
Period27/07/202129/07/2021
Internet address

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