Abstract

Bayesian optimization (BO) methods often rely on the assumption that the objective function is well-behaved, but in practice, this is seldom true for real-world objectives even if noise-free observations can be collected. Common approaches, which try to model the objective as precisely as possible, often fail to make progress by spending too many evaluations modeling irrelevant details. We address this issue by proposing surrogate models that focus on the well-behaved structure in the objective function, which is informative for search, while ignoring detrimental structure that is challenging to model from few observations. First, we demonstrate that surrogate models with appropriate noise distributions can absorb challenging structures in the objective function by treating them as irreducible uncertainty. Secondly, we show that a latent Gaussian process is an excellent surrogate for this purpose, comparing with Gaussian processes with standard noise distributions. We perform numerous experiments on a range of BO benchmarks and find that our approach improves reliability and performance when faced with challenging objective functions.

Original languageEnglish
Title of host publication37th International Conference on Machine Learning, ICML 2020
EditorsHal Daume, Aarti Singh
PublisherInternational Machine Learning Society (IMLS)
Pages947-956
Number of pages10
ISBN (Electronic)9781713821120
Publication statusPublished - 2020
Event37th International Conference on Machine Learning, ICML 2020 - Virtual, Online
Duration: 13 Jul 202018 Jul 2020

Publication series

Name37th International Conference on Machine Learning, ICML 2020
VolumePartF168147-2

Conference

Conference37th International Conference on Machine Learning, ICML 2020
CityVirtual, Online
Period13/07/2018/07/20

Bibliographical note

Funding Information:
This project was supported by Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnership, the Hans Werthn Fund at The Royal Swedish Academy of Engineering Sciences, EPSRC CDE (EP/L016540/1), EPSRC CAMERA project (EP/M023281/1), the German Federal Ministry of Education and Research (project 01 IS 18049 A), and the Royal Society.

Publisher Copyright:
© 2020 37th International Conference on Machine Learning, ICML 2020. All rights reserved.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Human-Computer Interaction
  • Software

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