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Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation

Berk, Julian, Gupta, Sunil, Rana, Santu and Venkatesh, Svetha 2021, Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation, in IJCAI-PRICAI-20 : Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, The Conference, [Yokohama, Japan], pp. 2284-2290, doi: 10.24963/ijcai.2020/316.

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Title Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation
Author(s) Berk, Julian
Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0002-3308-1930
Rana, SantuORCID iD for Rana, Santu orcid.org/0000-0003-2247-850X
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Artificial Intelligence and Seventeenth Pacific Rim International. Joint Conference on Artificial Intelligence (2021 : 29th : Yokohama, Japan)
Conference location Yokohama, Japan
Conference dates 7-15 Jan. 2021
Title of proceedings IJCAI-PRICAI-20 : Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Publication date 2021
Start page 2284
End page 2290
Total pages 7
Publisher The Conference
Place of publication [Yokohama, Japan]
Summary In order to improve the performance of Bayesian optimisation, we develop a modified Gaussian process upper confidence bound (GP-UCB) acquisition function. This is done by sampling the exploration-exploitation trade-off parameter from a distribution. We prove that this allows the expected trade-off parameter to be altered to better suit the problem without compromising a bound on the function's Bayesian regret. We also provide results showing that our method achieves better performance than GP-UCB in a range of real-world and synthetic problems.
ISBN 9780999241165
Language eng
DOI 10.24963/ijcai.2020/316
Indigenous content off
HERDC Research category E1 Full written paper - refereed
Grant ID FL170100006
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30142921

Document type: Conference Paper
Collections: Open Access Collection
A2I2 (Applied Artificial Intelligence Institute)
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.