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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
Towards Robust Off-Policy Evaluation via Human Inputs—0
Towards A Unified Policy Abstraction Theory and Representation Learning Approach in Markov Decision Processes—0
On the Reuse Bias in Off-Policy Reinforcement LearningCode0
Statistical Estimation of Confounded Linear MDPs: An Instrumental Variable Approach—0
Future-Dependent Value-Based Off-Policy Evaluation in POMDPsCode0
Conformal Off-policy PredictionCode0
Conformal Off-Policy Prediction in Contextual Bandits—0
Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks—0
Markovian Interference in Experiments—0
Hybrid Value Estimation for Off-policy Evaluation and Offline Reinforcement Learning—0
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