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An Entropy Search Portfolio for Bayesian Optimization

Abstract:

Portfolio methods provide an effective, principled way of combining a collection of acquisition functions in the context of Bayesian optimization. We introduce a novel approach to this problem motivated by an information theoretic consideration. Our construction additionally provides an extension of Thompson sampling to continuous domains with GP priors. We show that our method outperforms a range of other portfolio methods on several synthetic problems, automated machine learning tasks, and ...

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Publisher:
University of Oxford
Publication date:
2014-01-01
URN:
uuid:8af2df95-2a4e-40a7-869b-6daf0d9602fb
Local pid:
cs:8794

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