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Off-policy evaluation

Off-policy Evaluation (OPE), or offline evaluation in general, evaluates the performance of hypothetical policies leveraging only offline log data. It is particularly useful in applications where the online interaction involves high stakes and expensive setting such as precision medicine and recommender systems.

Papers

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Showing 41–50 of 265 papers

TitleStatusHype
Characterization of Efficient Influence Function for Off-Policy Evaluation Under Optimal Policies—0
Concept-driven Off Policy Evaluation—0
Confidence Interval for Off-Policy Evaluation from Dependent Samples via Bandit Algorithm: Approach from Standardized Martingales—0
Confident Natural Policy Gradient for Local Planning in q_π-realizable Constrained MDPs—0
Automated Off-Policy Estimator Selection via Supervised Learning—0
Conformal Off-Policy Evaluation in Markov Decision Processes—0
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning—0
Conformal Off-Policy Prediction in Contextual Bandits—0
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning—0
Causality and Batch Reinforcement Learning: Complementary Approaches To Planning In Unknown Domains—0
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