Publication result detail

Cross-Validated Off-Policy Evaluation

ČIEF, M.; KVETON, B.; KOMPAN, M.

Original Title

Cross-Validated Off-Policy Evaluation

English Title

Cross-Validated Off-Policy Evaluation

Type

Paper in proceedings (conference paper)

Original 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.

English 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.

Keywords

off-policy evaluation, cross-validation, estimator selection, hyper-parameter tuning

Key words in English

off-policy evaluation, cross-validation, estimator selection, hyper-parameter tuning

Authors

ČIEF, M.; KVETON, B.; KOMPAN, M.

RIV year

2026

Released

11.04.2025

Location

Pennsylvania

ISBN

978-1-57735-897-8

Book

Proceedings of the AAAI Conference on Artificial Intelligence

Pages from

16073

Pages to

16081

Pages count

9

URL

https://ojs.aaai.org/index.php/AAAI/article/view/33765

BibTex

@inproceedings{BUT197085,
  author="Matej {Čief} and  {} and Michal {Kompan}",
  title="Cross-Validated Off-Policy Evaluation",
  booktitle="Proceedings of the AAAI Conference on Artificial Intelligence",
  year="2025",
  pages="16073--16081",
  address="Pennsylvania",
  doi="10.1609/aaai.v39i15.33765",
  isbn="978-1-57735-897-8",
  url="https://ojs.aaai.org/index.php/AAAI/article/view/33765"
}

Documents

Cross_Validated_Off_Policy_Evaluation