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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 201–225 of 265 papers

TitleStatusHype
Accountable Off-Policy Evaluation With Kernel Bellman Statistics—0
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders—0
Statistical Bootstrapping for Uncertainty Estimation in Off-Policy Evaluation—0
Counterfactual Evaluation of Slate Recommendations with Sequential Reward InteractionsCode1
Off-Policy Evaluation via the Regularized Lagrangian—0
Off-Policy Exploitability-Evaluation in Two-Player Zero-Sum Markov Games—0
Strictly Batch Imitation Learning by Energy-based Distribution MatchingCode0
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance WeightingCode0
A maximum-entropy approach to off-policy evaluation in average-reward MDPs—0
Confidence Interval for Off-Policy Evaluation from Dependent Samples via Bandit Algorithm: Approach from Standardized Martingales—0
Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning—0
Causality and Batch Reinforcement Learning: Complementary Approaches To Planning In Unknown Domains—0
Taylor Expansion Policy Optimization—0
Batch Stationary Distribution EstimationCode0
Off-Policy Evaluation and Learning for External Validity under a Covariate ShiftCode0
Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation—0
Debiased Off-Policy Evaluation for Recommendation Systems—0
Adaptive Estimator Selection for Off-Policy EvaluationCode0
Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation—0
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning—0
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions—0
Minimax Value Interval for Off-Policy Evaluation and Policy Optimization—0
Safe Exploration for Optimizing Contextual BanditsCode0
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning—0
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation—0
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