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GLASSES: Relieving the myopia of Bayesian optimisation

Abstract:

We present glasses: Global optimisation with Look-Ahead through Stochastic Simulation and Expected-loss Search. The majority of global optimisation approaches in use are myopic, in only considering the impact of the next function value; the non-myopic approaches that do exist are able to consider only a handful of future evaluations. Our novel algorithm, glasses, permits the consideration of dozens of evaluations into the future. This is done by approximating the ideal look-ahead loss functio...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Oxford college:
Exeter College
Role:
Author
Publisher:
Journal of Machine Learning Research Publisher's website
Journal:
JMLR: Workshop and Conference Proceedings Journal website
Volume:
41
Pages:
790–799
Host title:
JMLR: Workshop and Conference Proceedings: Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS)
Publication date:
2016-01-01
Acceptance date:
2015-10-09
Event location:
Cadiz
Event start date:
2016-05-09T00:00:00Z
Event end date:
2016-05-11T00:00:00Z
ISSN:
1938-7228
Source identifiers:
664825
Pubs id:
pubs:664825
UUID:
uuid:40919a8e-fcd4-4ee2-80cf-9a9b77df23c5
Local pid:
pubs:664825
Deposit date:
2016-12-09

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