Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations

Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations

Gaussian processes (GP) are Bayesian non- parametric models that are widely used for probabilistic regression. Unfortunately, it cannot scale well with large data nor perform real-time predictions due to its cubic time cost in the data size. This paper presents two parallel GP regression methods tha...

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Bibliographic Details
Main Authors: Chen, Jie, Cao, Nannan, Low, Kian Hsiang, Ouyang, Ruofei, Colin Keng-Yan, Tan, Jaillet, Patrick
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Format: Article
Language:en_US
Published: Association for Uncertainty in Artificial Intelligence Press 2014
Online Access:http://hdl.handle.net/1721.1/87022
https://orcid.org/0000-0002-8585-6566

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http://hdl.handle.net/1721.1/87022
https://orcid.org/0000-0002-8585-6566

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