Bayesian Off-Policy Evaluation and Learning for Large Action Spaces
Abstract
In interactive systems, actions are often correlated, presenting an opportunity for more sample-efficient off-policy evaluation (OPE) and learning (OPL) in large action spaces. We introduce a unified Bayesian framework to capture these correlations through structured and informative priors. In this framework, we propose sDM, a generic Bayesian approach for OPE and OPL, grounded in both algorithmic and theoretical foundations. Notably, sDM leverages action correlations without compromising computational efficiency. Moreover, inspired by online Bayesian bandits, we introduce Bayesian metrics that assess the average performance of algorithms across multiple problem instances, deviating from the conventional worst-case assessments. We analyze sDM in OPE and OPL, highlighting the benefits of leveraging action correlations. Empirical evidence showcases the strong performance of sDM.
Cite
Text
Aouali et al. "Bayesian Off-Policy Evaluation and Learning for Large Action Spaces." Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, 2025.Markdown
[Aouali et al. "Bayesian Off-Policy Evaluation and Learning for Large Action Spaces." Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, 2025.](https://mlanthology.org/aistats/2025/aouali2025aistats-bayesian/)BibTeX
@inproceedings{aouali2025aistats-bayesian,
title = {{Bayesian Off-Policy Evaluation and Learning for Large Action Spaces}},
author = {Aouali, Imad and Brunel, Victor-Emmanuel and Rohde, David and Korba, Anna},
booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics},
year = {2025},
pages = {136-144},
volume = {258},
url = {https://mlanthology.org/aistats/2025/aouali2025aistats-bayesian/}
}