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Poster
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design
Desi Ivanova · Joel Jennings · Tom Rainforth · Cheng Zhang · Adam Foster

Thu Jul 27 01:30 PM -- 03:00 PM (PDT) @ Exhibit Hall 1 #117
in Poster Session 5 »

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.

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Author Information

Desi Ivanova (University of Oxford)
Joel Jennings (Microsoft)
Tom Rainforth (University of Oxford)
Cheng Zhang (Microsoft Research, Cambridge)
Adam Foster (Microsoft Research)

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