Review of recent advances in Gaussian process regression methods

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

Metadata

Item Type: Proceedings Paper
Authors/Creators:
Editors:
  • Panoutsos, G.
  • Mahfouf,, M.
  • Mihaylova, L.
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:
  • Published: 19 May 2024
  • Published (online): 19 May 2024
  • Accepted: 8 July 2022
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
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Open Archives Initiative ID (OAI ID):

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