Contextual Bayesian Optimization (CBO) is a powerful framework for optimizing
black-box, expensive-to-evaluate functions with respect to design variables,
while simultaneously efficiently integrating relevant contextual information
regarding the environment, such as experimental conditions. However, in many
practical scenarios, the relevance of contextual variables is not necessarily
known beforehand. Moreover, the contextual variables can sometimes be optimized
themselves, a setting that current CBO algorithms do not take into account.
Optimizing contextual variables may be costly, which raises the question of
determining a minimal relevant subset. In this paper, we frame this problem as
a cost-aware model selection BO task and address it using a novel method,
Sensitivity-Analysis-Driven Contextual BO (SADCBO). We learn the relevance of
context variables by sensitivity analysis of the posterior surrogate model at
specific input points, whilst minimizing the cost of optimization by leveraging
recent developments on early stopping for BO. We empirically evaluate our
proposed SADCBO against alternatives on synthetic experiments together with
extensive ablation studies, and demonstrate a consistent improvement across
examples.

本文介绍了一个基于敏感性分析的模型选择上下文优化算法（Sensitivity-Analysis-Driven Contextual BO），该算法可以在考虑环境上下文信息的同时最小化最优化成本，并在多个综合研究中表现出明显的改进。