We formalize the problem of contextual optimization through the lens of Bayesian experimental design and propose CO-BED -- a general, model-agnostic framework for designing contextual experiments using information-theoretic principles. After formulating a suitable information-based objective, we employ black-box variational methods to simultaneously estimate it and optimize the designs in a single stochastic gradient scheme. We further introduce a relaxation scheme to allow discrete actions to be accommodated. As a result, CO-BED provides a general and automated solution to a wide range of contextual optimization problems. We illustrate its effectiveness in a number of experiments, where CO-BED demonstrates competitive performance even when compared to bespoke, model-specific alternatives.

翻译：摘要：我们从贝叶斯实验设计的视角形式化地定义了上下文优化问题，并提出CO-BED——一种基于信息论原理设计上下文实验的通用、模型无关框架。在制定合适的信息论目标函数后，我们采用黑盒变分方法，通过单一随机梯度方案同时估计该目标函数并优化实验设计。我们进一步引入一种松弛方案以处理离散动作。因此，CO-BED为广泛的上下文优化问题提供了通用且自动化的解决方案。我们通过多项实验展示其有效性，结果表明，即使与特制的、模型特定的替代方案相比，CO-BED仍表现出具有竞争力的性能。