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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 51–60 of 265 papers

TitleStatusHype
Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation—0
Off-policy Evaluation with Deeply-abstracted StatesCode0
Automated Off-Policy Estimator Selection via Supervised Learning—0
Confident Natural Policy Gradient for Local Planning in q_π-realizable Constrained MDPs—0
Off-Policy Evaluation from Logged Human Feedback—0
RL in Latent MDPs is Tractable: Online Guarantees via Off-Policy Evaluation—0
A Fast Convergence Theory for Offline Decision Making—0
Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL PoliciesCode0
OPERA: Automatic Offline Policy Evaluation with Re-weighted Aggregates of Multiple Estimators—0
Cross-Validated Off-Policy EvaluationCode0
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