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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 181–190 of 265 papers

TitleStatusHype
Off-Policy Risk Assessment in Contextual Bandits—0
Infinite-Horizon Offline Reinforcement Learning with Linear Function Approximation: Curse of Dimensionality and Algorithm—0
Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds—0
Minimax Model Learning—0
Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach—0
Off-policy Confidence Sequences—0
Bootstrapping Fitted Q-Evaluation for Off-Policy Inference—0
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency—0
Minimax Off-Policy Evaluation for Multi-Armed Bandits—0
Smoothed functional-based gradient algorithms for off-policy reinforcement learning: A non-asymptotic viewpoint—0
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