English(EN) Curvature-aware Expected Free Energy as an Acquisition Function for Bayesian Optimization

新的贝叶斯优化方法使用期望自由能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-17 04:00

研究人员引入了一种新的贝叶斯优化采集函数，称为曲率感知期望自由能。该函数旨在通过同时优化和学习底层函数来解决联合学习和优化问题。在某些假设下，它可以简化为现有的方法，如上限置信界（Upper Confidence Bound）和期望信息增益（Expected Information Gain），并且已被证明可以为凹函数提供无偏收敛保证。所提出的方法在基准测试中的遗憾（regret）和均方误差（mean squared error）方面均表现出具有竞争力的性能。 AI

排序理由该集群包含一篇研究论文，详细介绍了贝叶斯优化的一种新采集函数。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Ajith Anil Meera, Wouter Kouw · 2026-09-17 04:00

面向贝叶斯优化的、感知曲率的期望自由能作为一种获取函数

arXiv:2603.26339v2 Announce Type: replace Abstract: We propose an Expected Free Energy-based acquisition function for Bayesian optimization to solve the joint learning and optimization problem, i.e., optimize and learn the underlying function simultaneously. We show that, under s…

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面向贝叶斯优化的、感知曲率的期望自由能作为一种获取函数

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