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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 141–150 of 265 papers

TitleStatusHype
Designing an Interpretable Interface for Contextual Bandits—0
Development and Validation of Heparin Dosing Policies Using an Offline Reinforcement Learning Algorithm—0
Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learning—0
Distributional Shift-Aware Off-Policy Interval Estimation: A Unified Error Quantification Framework—0
Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation—0
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation—0
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation—0
Doubly robust off-policy evaluation with shrinkage—0
Doubly-Robust Off-Policy Evaluation with Estimated Logging Policy—0
Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits—0
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