CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design

• URL: http://arxiv.org/abs/2302.14015v1
• Date: Mon, 27 Feb 2023 18:14:13 GMT
• Title: CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design
• Authors: Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster
• Abstract summary: CO-BED is a model-agnostic framework for designing contextual experiments using information-theoretic principles. As a result, CO-BED provides a general and automated solution to a wide range of contextual optimization problems.
• Score: 31.247108087199095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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