hypervolume

本文提出了一种用于不确定性下多目标优化的贝叶斯决策框架，强调利用高斯过程和主动学习策略进行期望超体积最大化。

This paper formalizes LLM configuration evaluation as a cost-aware multi-objective bandit problem, proposing a hypervolume-based UCB algorithm for online configuration selection and a cost-aware gap elimination algorithm for Pareto identification, both with theoretical guarantees and empirical validation.

本文介绍了 THV-UCB，一种用于带有板选择的多目标老虎机问题的算法，并建立了超体积遗憾的无间隙和依赖间隙的遗憾界。