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We develop parallel predictive entropy search (PPES), a novel algorithm forBayesian optimization of expensive black-box objective functions. At eachiteration, PPES aims to select a batch of points which will maximize theinformation gain about the global maximizer of the objective. Well knownstrategies exist for suggesting a single evaluation point based on previousobservations, while far fewer are known for selecting batches of points toevaluate in parallel. The few batch selection schemes that have been studiedall resort to greedy methods to compute an optimal batch. To the best of ourknowledge, PPES is the first non-greedy batch Bayesian optimization strategy.We demonstrate the benefit of this approach in optimization performance on bothsynthetic and real world applications, including problems in machine learning,rocket science and robotics.

Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions