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.

翻译：上下文贝叶斯优化（Contextual Bayesian Optimization, CBO）是一种强大框架，用于优化关于设计变量的黑盒、高代价函数，同时高效整合与实验条件等环境相关的上下文信息。然而，在许多实际场景中，上下文变量的相关性并非事先已知。此外，上下文变量本身有时也可以被优化，而当前的CBO算法并未考虑这一设定。优化上下文变量可能代价高昂，这就引发了确定最小相关子集的问题。本文将这一问题建模为成本感知的模型选择贝叶斯优化任务，并提出一种新方法——灵敏度分析驱动的上下文贝叶斯优化（Sensitivity-Analysis-Driven Contextual BO, SADCBO）来加以解决。我们通过对特定输入点处的后验代理模型进行灵敏度分析来学习上下文变量的相关性，同时利用贝叶斯优化中早期停止的最新进展来最小化优化成本。我们通过合成实验以及广泛的消融研究，将所提出的SADCBO与多种替代方法进行经验评估，并展示了其在各示例中一致性的改进效果。