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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 251–265 of 265 papers

TitleStatusHype
Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation—0
Minimax Weight and Q-Function Learning for Off-Policy Evaluation—0
Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization—0
Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol—0
More Efficient Off-Policy Evaluation through Regularized Targeted Learning—0
More Robust Doubly Robust Off-policy Evaluation—0
Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds—0
Offline Comparison of Ranking Functions using Randomized Data—0
Offline Policy Evaluation and Optimization under Confounding—0
Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information—0
Off-policy Confidence Sequences—0
Off-policy estimation with adaptively collected data: the power of online learning—0
Off-Policy Evaluation and Counterfactual Methods in Dynamic Auction Environments—0
Off-Policy Evaluation and Learning for the Future under Non-Stationarity—0
Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy—0
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