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Parallel predictive entropy search for batch global optimization of expensive objective functions

Shah, A and Ghahramani, Z (2015) Parallel predictive entropy search for batch global optimization of expensive objective functions. Advances in Neural Information Processing Systems, 2015-J. pp. 3330-3338. ISSN 1049-5258

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Abstract

We develop parallel predictive entropy search (PPES), a novel algorithm for Bayesian optimization of expensive black-box objective functions. At each iteration, PPES aims to select a batch of points which will maximize the information gain about the global maximizer of the objective. Well known strategies exist for suggesting a single evaluation point based on previous observations, while far fewer are known for selecting batches of points to evaluate in parallel. The few batch selection schemes that have been studied all resort to greedy methods to compute an optimal batch. To the best of our knowledge, PPES is the first nongreedy batch Bayesian optimization strategy. We demonstrate the benefit of this approach in optimization performance on both synthetic and real world applications, including problems in machine learning, rocket science and robotics.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:34
Last Modified: 03 Aug 2017 03:08
DOI: