Conference item
Asynchronous batch Bayesian optimisation with improved local penalisation
- Abstract:
- Batch Bayesian optimisation (BO) has been successfully applied to hyperparameter tuning using parallel computing, but it is wasteful of resources: workers that complete jobs ahead of others are left idle. We address this problem by developing an approach. Penalising Locally for Asynchronous Bayesian Optimisation on k workers (PLAyBOOK), for asynchronous parallel BO. We demonstrate empirically the efficacy of PLAyBOOK and its variants on synthetic tasks and a real-world problem. We undertake a comparison between synchronous and asynchronous BO, and show that asynchronous BO often outperforms synchronous batch BO in both wall-clock time and number of function evaluations.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 7.2MB, Terms of use)
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- Publication website:
- https://proceedings.mlr.press/v97/alvi19a.html
Authors
- Publisher:
- Proceedings of Machine Learning Research
- Host title:
- Proceedings of the 36th International Conference on Machine Learning
- Volume:
- 97
- Pages:
- 253-262
- Publication date:
- 2019-05-24
- Acceptance date:
- 2019-04-24
- Event title:
- The Thirty-Sixth International Conference on Machine Learning
- Event location:
- Long Beach, California, USA
- Event website:
- https://icml.cc/Conferences/2019
- Event start date:
- 2019-06-09
- Event end date:
- 2019-06-15
- EISSN:
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2640-3498
- Language:
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English
- Pubs id:
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1085797
- Local pid:
-
pubs:1085797
- Deposit date:
-
2026-10-22
- ARK identifier:
Terms of use
- Copyright holder:
- Alvi et al
- Copyright date:
- 2019
- Rights statement:
- ©2019 The Authors. This paper is an open access article distributed under the terms of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
- Notes:
- This paper was presented at the 36th International Conference on Machine Learning, 9-15 June 2019, Long Beach, California, USA.
- Licence:
- CC Attribution (CC BY)
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