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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 161–170 of 265 papers

TitleStatusHype
Towards Hyperparameter-free Policy Selection for Offline Reinforcement LearningCode0
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
Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation—0
Data-Driven Off-Policy Estimator Selection: An Application in User Marketing on An Online Content Delivery Service—0
State Relevance for Off-Policy EvaluationCode0
Debiasing Samples from Online Learning Using Bootstrap—0
Online Learning for Recommendations at Grubhub—0
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