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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
Anytime-valid off-policy inference for contextual banditsCode1
Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model—0
A Policy-Guided Imitation Approach for Offline Reinforcement LearningCode1
Off-Policy Evaluation for Episodic Partially Observable Markov Decision Processes under Non-Parametric Models—0
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
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