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Exploration enhanced expected improvement for Bayesian optimization

Version 2 2024-06-03, 06:43
Version 1 2019-03-01, 14:09
conference contribution
posted on 2024-06-03, 06:43 authored by Julian BerkJulian Berk, V Nguyen, Sunil GuptaSunil Gupta, Santu RanaSantu Rana, Svetha VenkateshSvetha Venkatesh
Bayesian optimization (BO) is a sample-efficient method for global optimization of expensive, noisy, black-box functions using probabilistic methods. The performance of a BO method depends on its selection strategy through an acquisition function. This must balance improving our understanding of the function in unknown regions (exploration) with locally improving on known promising samples (exploitation). Expected improvement (EI) is one of the most widely used acquisition functions for BO. Unfortunately, it has a tendency to over-exploit, meaning that it can be slow in finding new peaks. We propose a modification to EI that will allow for increased early exploration while providing similar exploitation once the system has been suitably explored. We also prove that our method has a sub-linear convergence rate and test it on a range of functions to compare its performance against the standard EI and other competing methods. Code related to this paper is available at: https://github.com/jmaberk/BO_with_E3I.

History

Volume

11052

Pagination

621-637

Location

Dublin, Ireland

Start date

2018-09-10

End date

2018-09-14

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783030109271

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2019, Springer Nature Switzerland AG

Editor/Contributor(s)

Berlingerio M, Bonchi F, Gärtner T, Hurley N, Ifrim G

Title of proceedings

ECML-PKDD 2018 : Proceedings of the e European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2018

Event

European Machine Learning and Data Mining. Conference (2018 : Dublin, Ireland)

Publisher

Springer

Place of publication

Cham, Switzerland

Series

European Machine Learning and Data Mining Conference

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