Lyu, C., Liu, X. and Mihaylova, L. orcid.org/0000-0001-5856-2223 (2024) Review of recent advances in Gaussian process regression methods. In: Panoutsos, G., Mahfouf,, M. and Mihaylova, L., (eds.) Advances in Computational Intelligence Systems. UKCI 2022. UKCI'2022 - 21st UK Workshop on Computational Intelligence, 07-09 Sep 2022, Sheffield, UK. Advances in Intelligent Systems and Computing, 1454 . Springer Nature , pp. 226-237. ISBN 978-3-031-55567-1
Abstract
Gaussian process (GP) methods have been widely studied recently, especially for large-scale systems with big data and even more extreme cases when data is sparse. Key advantages of these methods consist in: 1) the ability to provide inherent ways to assess the impact of uncertainties (especially in the data, and environment) on the solutions, 2) have efficient factorisation based implementations and 3) can be implemented easily in distributed manners and hence provide scalable solutions. This paper reviews the recently developed key factorised GP methods such as the hierarchical off-diagonal low-rank approximation methods and GP with Kronecker structures. An example illustrates the performance of these methods with respect to accuracy and computational complexity.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. Except as otherwise noted, this author-accepted version of a paper published in Advances in Intelligent Systems and Computing is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Gaussian process; factorisation; covariance matrix; hierarchical off-diagonal matrix; low-rank approximation |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council EP/T013265/1; EP/V026747/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 09 Aug 2022 10:38 |
Last Modified: | 22 May 2024 09:49 |
Status: | Published |
Publisher: | Springer Nature |
Series Name: | Advances in Intelligent Systems and Computing |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-031-55568-8_19 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:189771 |