AAAI 2025technical4 citations
Cross-Validated Off-Policy Evaluation
Matej Cief, Branislav Kveton, Michal Kompan
Abstract
We study estimator selection and hyper-parameter tuning in off-policy evaluation. Although cross-validation is the most popular method for model selection in supervised learning, off-policy evaluation relies mostly on theory, which provides only limited guidance to practitioners. We show how to use cross-validation for off-policy evaluation. This challenges a popular belief that cross-validation in off-policy evaluation is not feasible. We evaluate our method empirically and show that it addresses a variety of use cases.
BibTeX
@article{Cief_Kveton_Kompan_2025, title={Cross-Validated Off-Policy Evaluation}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/33765}, DOI={10.1609/aaai.v39i15.33765}, abstractNote={We study estimator selection and hyper-parameter tuning in off-policy evaluation. Although cross-validation is the most popular method for model selection in supervised learning, off-policy evaluation relies mostly on theory, which provides only limited guidance to practitioners. We show how to use cross-validation for off-policy evaluation. This challenges a popular belief that cross-validation in off-policy evaluation is not feasible. We evaluate our method empirically and show that it addresses a variety of use cases.}, number={15}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Cief, Matej and Kveton, Branislav and Kompan, Michal}, year={2025}, month={Apr.}, pages={16073-16081} }