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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 111–120 of 265 papers

TitleStatusHype
Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions—0
Large-scale Validation of Counterfactual Learning Methods: A Test-Bed—0
Limit Order Book Simulation and Trade Evaluation with K-Nearest-Neighbor Resampling—0
Infinite-Horizon Offline Reinforcement Learning with Linear Function Approximation: Curse of Dimensionality and Algorithm—0
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions—0
IntOPE: Off-Policy Evaluation in the Presence of Interference—0
Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation—0
Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy—0
Deep Jump Q-Evaluation for Offline Policy Evaluation in Continuous Action Space—0
Debiasing Samples from Online Learning Using Bootstrap—0
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