Cross-Validated Off-Policy Evaluation
<|reference_start|>Cross-Validated Off-Policy Evaluation: In this paper, we study the problem of 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-based approaches, which provide 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.<|reference_end|>
@article{cief2024cross-validated,
    title={Cross-Validated Off-Policy Evaluation},
    author={Matej Cief, Branislav Kveton, Michal Kompan},
    journal={arXiv preprint arXiv:2405.15332},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15332},
    primaryClass={cs.LG}
}