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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 131–140 of 265 papers

TitleStatusHype
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling—0
Variance-Aware Off-Policy Evaluation with Linear Function Approximation—0
Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits—0
Weighted model estimation for offline model-based reinforcement learning—0
Why Should I Trust You, Bellman? Evaluating the Bellman Objective with Off-Policy Data—0
Data-Driven Off-Policy Estimator Selection: An Application in User Marketing on An Online Content Delivery Service—0
Data Poisoning Attacks on Off-Policy Policy Evaluation Methods—0
Debiasing Samples from Online Learning Using Bootstrap—0
Deep Jump Q-Evaluation for Offline Policy Evaluation in Continuous Action Space—0
Defining Admissible Rewards for High Confidence Policy Evaluation—0
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