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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 41–50 of 265 papers

TitleStatusHype
Long-term Off-Policy Evaluation and LearningCode0
Hyperparameter Optimization Can Even be Harmful in Off-Policy Learning and How to Deal with It—0
Data Poisoning Attacks on Off-Policy Policy Evaluation Methods—0
Methodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilation—0
Doubly-Robust Off-Policy Evaluation with Estimated Logging Policy—0
Predictive Performance Comparison of Decision Policies Under ConfoundingCode0
Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision ProcessesCode0
Cramming Contextual Bandits for On-policy Statistical Evaluation—0
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces—0
On the Curses of Future and History in Future-dependent Value Functions for Off-policy Evaluation—0
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