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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 161–170 of 265 papers

TitleStatusHype
Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making—0
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare—0
Hybrid Value Estimation for Off-policy Evaluation and Offline Reinforcement Learning—0
Hyperparameter Optimization Can Even be Harmful in Off-Policy Learning and How to Deal with It—0
Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves—0
Infinite-Horizon Offline Reinforcement Learning with Linear Function Approximation: Curse of Dimensionality and Algorithm—0
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions—0
IntOPE: Off-Policy Evaluation in the Presence of Interference—0
Large-scale Validation of Counterfactual Learning Methods: A Test-Bed—0
Off-Policy Evaluation with Online Adaptation for Robot Exploration in Challenging Environments—0
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