  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 non-greedy 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.
