A hierarchical expected improvement method for Bayesian optimization
- 2024-02-02 (Fri.), 10:30 AM
- 統計所B1演講廳;茶 會:上午10:10。
- 英文演講,實體與線上視訊同步進行。
- Academician Jeff Wu ( 吳建福 院士 )
- H. Milton Stewart School of Industrial and Systems Engineering College of Engineering, Georgia Institute of Technology
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 Gaussian process model, which results in suboptimal solutions. 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. Under certain prior specifications, we prove the global convergence of HEI over a broad function space, and derive global convergence rates under smoothness assumptions on the objective function. We then introduce hyperparameter estimation methods which allow HEI to mimic a fully Bayesian procedure while avoiding expensive Markov-chain Monte Carlo sampling. Numerical experiments and a toy semiconductor optimization application show the improvement of HEI over existing black-box optimization methods.
(Authors: Zhehui Chen, Simon Mak, and C. F. Jeff Wu; to appear in JASA T&M)線上視訊請點選連結
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最後更新日期:2024-01-31 12:22