Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization

Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, Masayuki Karasuyama

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Citations (Scopus)

Abstract

In a standard setting of Bayesian optimization (BO), the objective function evaluation is assumed to be highly expensive. Multifidelity Bayesian optimization (MFBO) accelerates BO by incorporating lower fidelity observations available with a lower sampling cost. We propose a novel information-theoretic approach to MFBO, called multi-fidelity max-value entropy search (MF-MES), that enables us to obtain a more reliable evaluation of the information gain compared with existing information-based methods for MFBO. Further, we also propose a parallelization of MF-MES mainly for the asynchronous setting because queries typically occur asynchronously in MFBO due to a variety of sampling costs. We show that most of computations in our acquisition functions can be derived analytically, except for at most only two dimensional numerical integration that can be performed efficiently by simple approximations. We demonstrate effectiveness of our approach by using benchmark datasets and a real-world application to materials science data.

Original languageEnglish
Title of host publication37th International Conference on Machine Learning, ICML 2020
EditorsHal Daume, Aarti Singh
PublisherInternational Machine Learning Society (IMLS)
Pages9276-9287
Number of pages12
ISBN (Electronic)9781713821120
Publication statusPublished - 2020
Event37th International Conference on Machine Learning, ICML 2020 - Virtual, Online
Duration: 2020 Jul 132020 Jul 18

Publication series

Name37th International Conference on Machine Learning, ICML 2020
VolumePartF168147-12

Conference

Conference37th International Conference on Machine Learning, ICML 2020
CityVirtual, Online
Period20/7/1320/7/18

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