CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design
This provides a general solution for contextual optimization problems, though it appears incremental as it builds on existing principles.
The paper tackles the problem of contextual optimization by proposing CO-BED, a model-agnostic framework based on Bayesian experimental design and information theory, which shows competitive performance in experiments against specialized methods.
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. In addition, to accommodate discrete actions within our framework, we propose leveraging continuous relaxation schemes, which can naturally be integrated into our variational objective. 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.