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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
Conformal Off-Policy Evaluation in Markov Decision Processes—0
Conformal Off-Policy Prediction in Contextual Bandits—0
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning—0
Consistent On-Line Off-Policy Evaluation—0
Counterfactual Analysis in Dynamic Latent State Models—0
Counterfactual Learning with General Data-generating Policies—0
Data-Driven Off-Policy Estimator Selection: An Application in User Marketing on An Online Content Delivery Service—0
Data Poisoning Attacks on Off-Policy Policy Evaluation Methods—0
Debiasing Samples from Online Learning Using Bootstrap—0
Deep Jump Q-Evaluation for Offline Policy Evaluation in Continuous Action Space—0
Defining Admissible Rewards for High Confidence Policy Evaluation—0
Designing an Interpretable Interface for Contextual Bandits—0
Development and Validation of Heparin Dosing Policies Using an Offline Reinforcement Learning Algorithm—0
Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learning—0
Distributional Shift-Aware Off-Policy Interval Estimation: A Unified Error Quantification Framework—0
Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation—0
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation—0
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation—0
Doubly robust off-policy evaluation with shrinkage—0
Doubly-Robust Off-Policy Evaluation with Estimated Logging Policy—0
Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits—0
Efficient Counterfactual Learning from Bandit Feedback—0
Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning—0
Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning—0
Efron-Stein PAC-Bayesian Inequalities—0
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