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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 121–130 of 265 papers

TitleStatusHype
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning—0
Expected Sarsa(λ) with Control Variate for Variance Reduction—0
Enhancing Offline Model-Based RL via Active Model Selection: A Bayesian Optimization Perspective—0
Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment—0
Conformal Off-Policy Prediction in Contextual Bandits—0
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization—0
An Instrumental Variable Approach to Confounded Off-Policy Evaluation—0
Accountable Off-Policy Evaluation via a Kernelized Bellman Statistics—0
Emphatic TD Bellman Operator is a Contraction—0
Efron-Stein PAC-Bayesian Inequalities—0
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