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 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 \emph{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）能够高效优化关于设计变量且同时整合环境相关上下文信息（如实验条件）的黑箱函数。然而，上下文变量的相关性并非事先已知。此外，上下文变量有时可以在额外成本下进行自身优化，当前CBO算法忽视了这一设定。成本敏感的CBO会简单地将可优化的上下文变量基于其成本纳入设计变量。相反，我们根据上下文变量的相关性与将其优化（相较于由环境决定）所产生的额外成本之间的权衡，自适应地选择参与优化的上下文变量子集。通过后验代理模型的灵敏度分析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最小化优化成本。我们在合成实验和真实实验中将所提出的灵敏度分析驱动上下文贝叶斯优化（SADCBO）方法与替代方案进行实证评估，并辅以大量消融研究，证明该方法在所有示例中均能实现一致性的性能提升。