Scalable Gaussian process inference with finite-data mean and variance guarantees

Scalable Gaussian process inference with finite-data mean and variance guarantees

Gaussian processes (GPs) offer a flexible class of priors for nonparametric Bayesian regression, but popular GP posterior inference methods are typically prohibitively slow or lack desirable finite-data guarantees on quality. We develop a scalable approach to approximate GP regression, with finite-d...

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Bibliographic Details
Main Authors: Huggins, Jonathan H., Broderick, Tamara A, Campbell, Trevor David
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Format: Article
Language:English
Published: PMLR 2020
Online Access:https://hdl.handle.net/1721.1/128771

Internet

https://hdl.handle.net/1721.1/128771

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