Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding the environment, such as experimental conditions. However, the relevance of contextual variables is not necessarily known beforehand. Moreover, contextual variables can sometimes be optimized themselves at an additional cost, a setting overlooked by current CBO algorithms. Cost-sensitive CBO would simply include optimizable contextual variables as part of the design variables based on their cost. Instead, we adaptively select a subset of contextual variables to include in the optimization, based on the trade-off between their relevance and the additional cost incurred by optimizing them compared to leaving them to be determined by the environment. We learn the relevance of contextual variables by sensitivity analysis of the posterior surrogate model while minimizing the cost of optimization by leveraging recent developments on early stopping for BO. We empirically evaluate our proposed Sensitivity-Analysis-Driven Contextual BO (SADCBO) method against alternatives on both synthetic and real-world experiments, together with extensive ablation studies, and demonstrate a consistent improvement across examples.

翻译：上下文贝叶斯优化（CBO）能高效地相对于设计变量优化黑箱函数，同时整合关于环境（如实验条件）的上下文信息。然而，上下文变量的相关性未必事先已知。此外，上下文变量有时可以以额外成本为代价进行优化，这一设定被当前CBO算法所忽视。成本敏感的CBO会简单地根据成本将可优化的上下文变量作为设计变量的一部分。相反，我们根据上下文变量的相关性与其优化成本（相较于由环境决定）之间的权衡，自适应地选择包含在优化中的上下文变量子集。我们通过后验代理模型的敏感性分析来学习上下文变量的相关性，同时利用贝叶斯优化早期停止的最新进展来最小化优化成本。我们在合成实验和真实世界实验中，对所提出的敏感性分析驱动的上下文贝叶斯优化（SADCBO）方法与替代方法进行了实证评估，并进行了广泛的消融研究，结果表明该方法在不同示例中均能实现持续改进。