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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 201–210 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
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