FigBO: A Generalized Acquisition Function Framework with Look-Ahead
Capability for Bayesian Optimization
Hui Chen Xuhui Fan Zhangkai Wu Longbing Cao
作者信息
Abstract
Bayesian optimization is a powerful technique for optimizing
expensive-to-evaluate black-box functions, consisting of two main components: a
surrogate model and an acquisition function. In recent years, myopic
acquisition functions have been widely adopted for their simplicity and
effectiveness. However, their lack of look-ahead capability limits their
performance. To address this limitation, we propose FigBO, a generalized
acquisition function that incorporates the future impact of candidate points on
global information gain. FigBO is a plug-and-play method that can integrate
seamlessly with most existing myopic acquisition functions. Theoretically, we
analyze the regret bound and convergence rate of FigBO when combined with the
myopic base acquisition function expected improvement (EI), comparing them to
those of standard EI. Empirically, extensive experimental results across
diverse tasks demonstrate that FigBO achieves state-of-the-art performance and
significantly faster convergence compared to existing methods.引用本文复制引用
Hui Chen,Xuhui Fan,Zhangkai Wu,Longbing Cao.FigBO: A Generalized Acquisition Function Framework with Look-Ahead
Capability for Bayesian Optimization[EB/OL].(2025-04-28)[2026-04-05].https://arxiv.org/abs/2504.20307.学科分类
计算技术、计算机技术
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