@inproceedings{ivanova2023co-bed,
author = {Ivanova, Desi R. and Jennings, Joel and Rainforth, Tom and Zhang, Cheng and Foster, Adam},
title = {CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design},
booktitle = {ICML 2023},
year = {2023},
month = {June},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/co-bed-information-theoretic-contextual-optimization-via-bayesian-experimental-design/},
}