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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 171–180 of 265 papers

TitleStatusHype
A Unified Off-Policy Evaluation Approach for General Value Function—0
Supervised Off-Policy RankingCode0
Variance-Aware Off-Policy Evaluation with Linear Function Approximation—0
Control Variates for Slate Off-Policy EvaluationCode0
Robust Generalization despite Distribution Shift via Minimum Discriminating InformationCode0
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy EvaluationCode0
Deeply-Debiased Off-Policy Interval EstimationCode0
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization—0
Universal Off-Policy EvaluationCode0
Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learning—0
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