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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 226–250 of 265 papers

TitleStatusHype
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
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation—0
Adaptive Trade-Offs in Off-Policy Learning—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
Balanced off-policy evaluation in general action spaces—0
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement LearningCode0
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
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal ModelsCode0
Combining Parametric and Nonparametric Models for Off-Policy Evaluation—0
Privacy Preserving Off-Policy Evaluation—0
Off-Policy Evaluation of Probabilistic Identity Data in Lookalike Modeling—0
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