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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 101–110 of 265 papers

TitleStatusHype
Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation—0
Adaptive Trade-Offs in Off-Policy Learning—0
Counterfactual Analysis in Dynamic Latent State Models—0
Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation—0
Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making—0
Generalized Emphatic Temporal Difference Learning: Bias-Variance Analysis—0
Balanced off-policy evaluation in general action spaces—0
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare—0
Consistent On-Line Off-Policy Evaluation—0
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency—0
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