Cross-Validated Off-Policy Evaluation
Machine Learning
2024-12-23 v4
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.
Cite
@article{arxiv.2405.15332,
title = {Cross-Validated Off-Policy Evaluation},
author = {Matej Cief and Branislav Kveton and Michal Kompan},
journal= {arXiv preprint arXiv:2405.15332},
year = {2024}
}
Comments
13 pages, 7 figures, to be published in AAAI 2025