Preprintgreen2015

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

108 citations0/0 files storedarXiv (Cornell University)

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Advanced Bandit Algorithms Research Advanced Multi-Objective Optimization Algorithms Gaussian Processes and Bayesian Inference

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 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.

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References

2012Practical Bayesian Optimization of Machine Learning Algorithms 2012Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting 2010A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning 2015Scalable Bayesian Optimization Using Deep Neural Networks 2012Entropy search for information-efficient global optimization 2014Predictive Entropy Search for Efficient Global Optimization of Black-box Functions 2012Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization 2008Practical Bayesian optimization

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