Event Date
Speaker: Simon Mak (Assistant Professor, Statistical Science, Duke University)
Title: "A hierarchical expected improvement method for Bayesian optimization"
Abstract: The Expected Improvement (EI) method is a widely-used Bayesian optimization method, which makes use of a fitted Gaussian process model for efficient black-box optimization. However, one key drawback of EI is that it is overly greedy in exploiting the fitted model for optimization, resulting in suboptimal solutions even with large sample sizes. To address this, we propose a new hierarchical EI (HEI) framework, which makes use of a hierarchical Gaussian process model. HEI preserves a closed-form acquisition function, and corrects the over-greediness of EI by encouraging exploration of the optimization space. We introduce hyperparameter estimation methods which allow HEI to mimic a fully Bayesian optimization procedure, while avoiding expensive Markov-chain Monte Carlo sampling steps. We prove the global convergence of HEI over a broad function space, and establish near-minimax convergence rates under certain prior specifications. Numerical experiments show the improvement of HEI over existing Bayesian optimization methods, for synthetic functions and a semiconductor manufacturing optimization problem.
About the speaker: Simon Mak, is an assistant professor in the department of statistical science at Duke University. His research interests include the reduction of big and high-dimensional data, scalable Bayesian methods, computer experiments, and Monte Carlo and Quasi-Monte Carlo sampling.
Time/Date: Thursday, June 3, 2021, 4:10pm