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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 91–100 of 265 papers

TitleStatusHype
Doubly Robust Estimator for Off-Policy Evaluation with Large Action SpacesCode0
On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-n RecommendationCode0
The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation—0
Hindsight-DICE: Stable Credit Assignment for Deep Reinforcement LearningCode0
Leveraging Factored Action Spaces for Off-Policy EvaluationCode0
Off-policy Evaluation in Doubly Inhomogeneous EnvironmentsCode0
K-Nearest-Neighbor Resampling for Off-Policy Evaluation in Stochastic ControlCode0
Counterfactual Evaluation of Peer-Review Assignment PoliciesCode0
Human Choice Prediction in Language-based Persuasion Games: Simulation-based Off-Policy EvaluationCode0
Scalable and Safe Remediation of Defective Actions in Self-Learning Conversational Systems—0
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