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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 151–160 of 265 papers

TitleStatusHype
Off-Policy Evaluation in Partially Observed Markov Decision Processes under Sequential Ignorability—0
Stateful Offline Contextual Policy Evaluation and Learning—0
Why Should I Trust You, Bellman? Evaluating the Bellman Objective with Off-Policy Data—0
A Spectral Approach to Off-Policy Evaluation for POMDPs—0
Data-Driven Off-Policy Estimator Selection: An Application in User Marketing on An Online Content Delivery Service—0
Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation—0
State Relevance for Off-Policy EvaluationCode0
Evaluating the Robustness of Off-Policy EvaluationCode1
Debiasing Samples from Online Learning Using Bootstrap—0
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare SettingsCode1
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