An Entropy Search Portfolio for Bayesian Optimization

Abstract

Bayesian optimization is a sample-efficient method for black-box global optimization. How- ever, the performance of a Bayesian optimization method very much depends on its exploration strategy, i.e. the choice of acquisition function, and it is not clear a priori which choice will result in superior performance. While portfolio methods provide an effective, principled way of combining a collection of acquisition functions, they are often based on measures of past performance which can be misleading. To address this issue, we introduce the Entropy Search Portfolio (ESP): a novel approach to portfolio construction which is motivated by information theoretic considerations. We show that ESP outperforms existing portfolio methods on several real and synthetic problems, including geostatistical datasets and simulated control tasks. We not only show that ESP is able to offer performance as good as the best, but unknown, acquisition function, but surprisingly it often gives better performance. Finally, over a wide range of conditions we find that ESP is robust to the inclusion of poor acquisition functions.

10 pages, 5 figures

References in corpus (4)

• Practical Bayesian Optimization of Machine Learning Algorithms
• A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
• Predictive Entropy Search for Efficient Global Optimization of Black-box Functions
• New inference strategies for solving Markov Decision Processes using reversible jump MCMC

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• Constrained Bayesian Optimization for Automatic Chemical Design
• Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
• Heteroscedastic Treed Bayesian Optimisation
• Bayesian Optimization using Deep Gaussian Processes
• Minimum Regret Search for Single- and Multi-Task Optimization
• Active Contextual Entropy Search
• Uncertainty Quantification for Bayesian Optimization
• New Heuristics for Parallel and Scalable Bayesian Optimization