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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
Bellman Residual Orthogonalization for Offline Reinforcement Learning—0
Off-Policy Evaluation in Embedded Spaces—0
Off-Policy Evaluation with Policy-Dependent Optimization Response—0
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided MarketsCode0
Doubly Robust Distributionally Robust Off-Policy Evaluation and LearningCode1
Off-Policy Evaluation for Large Action Spaces via EmbeddingsCode2
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory—0
Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior ModelCode2
Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making—0
On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation—0
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