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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
Consistent On-Line Off-Policy Evaluation—0
Minimax Model Learning—0
Hybrid Value Estimation for Off-policy Evaluation and Offline Reinforcement Learning—0
Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making—0
Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves—0
An Instrumental Variable Approach to Confounded Off-Policy Evaluation—0
Counterfactual Analysis in Dynamic Latent State Models—0
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare—0
Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation—0
Deep Jump Q-Evaluation for Offline Policy Evaluation in Continuous Action Space—0
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