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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 201–250 of 265 papers

TitleStatusHype
Deep Jump Q-Evaluation for Offline Policy Evaluation in Continuous Action Space—0
Accountable Off-Policy Evaluation With Kernel Bellman Statistics—0
Statistical Bootstrapping for Uncertainty Estimation in Off-Policy Evaluation—0
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders—0
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
Accountable Off-Policy Evaluation via a Kernelized Bellman Statistics—0
More Efficient Off-Policy Evaluation through Regularized Targeted Learning—0
Triply Robust Off-Policy Evaluation—0
Minimax Weight and Q-Function Learning for Off-Policy Evaluation—0
From Importance Sampling to Doubly Robust Policy GradientCode0
Adaptive Trade-Offs in Off-Policy Learning—0
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation—0
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling—0
Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning—0
Off-Policy Evaluation in Partially Observable Environments—0
Efron-Stein PAC-Bayesian Inequalities—0
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision ProcessesCode0
Doubly robust off-policy evaluation with shrinkage—0
Task Selection Policies for Multitask Learning—0
Expected Sarsa(λ) with Control Variate for Variance Reduction—0
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement LearningCode0
Balanced off-policy evaluation in general action spaces—0
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling—0
Off-Policy Evaluation via Off-Policy Classification—0
Defining Admissible Rewards for High Confidence Policy Evaluation—0
Semi-Parametric Efficient Policy Learning with Continuous ActionsCode0
Combining Parametric and Nonparametric Models for Off-Policy Evaluation—0
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal ModelsCode0
Privacy Preserving Off-Policy Evaluation—0
Off-Policy Evaluation of Probabilistic Identity Data in Lookalike Modeling—0
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