2025
AAAI
AAAI 2025
Cross-Validated Off-Policy Evaluation
Abstract
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.
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Interdisciplinary Bridge
- Artificial Intelligence and Reinforcement Learning
Authors
Topics
Machine Learning > Optimization & Theory > Statistical Learning
Reinforcement Learning > Methods > Offline RL
Machine Learning > Learning Types > Reinforcement Learning
Machine Learning > Optimization & Theory > Stochastic Methods
Machine Learning > Learning Types > Evaluation
Machine Learning > Learning Types > Offline Reinforcement Learning