提出可前瞻决策的贝叶斯优化框架，提升搜索效率与收敛速度。
FigBO: A Generalized Acquisition Function Framework with Look-Ahead Capability for Bayesian Optimization
- 引入前瞻性机制，评估候选点对全局信息增益的影响
- 在多种任务上实现领先性能，收敛速度显著快于现有方法
- 兼容主流贪心采集函数，无需重构原有流程
贝叶斯优化是优化昂贵黑箱函数的强大工具，包含代理模型和采集函数两大组件。近年来，贪心型采集函数因简洁高效被广泛应用，但其缺乏前瞻能力限制了性能。为此，我们提出FigBO，一种能纳入候选点未来影响的通用采集函数框架。FigBO可无缝集成至多数现有贪心采集函数中，作为即插即用模块。理论上，我们分析了当与期望改进（EI）基线结合时，FigBO的累积遗憾界与收敛速率，并与标准EI进行对比。实验表明，在多样任务上，FigBO均达到当前最优表现，且收敛速度显著优于已有方法。
原文摘要 · Abstract (English)
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.
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