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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 26–50 of 265 papers

TitleStatusHype
IntOPE: Off-Policy Evaluation in the Presence of Interference—0
Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits—0
Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment—0
Causal Deepsets for Off-policy Evaluation under Spatial or Spatio-temporal InterferencesCode0
Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation—0
Off-policy Evaluation with Deeply-abstracted StatesCode0
Confident Natural Policy Gradient for Local Planning in q_π-realizable Constrained MDPs—0
Automated Off-Policy Estimator Selection via Supervised Learning—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
Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and LearningCode0
Long-term Off-Policy Evaluation and LearningCode0
Hyperparameter Optimization Can Even be Harmful in Off-Policy Learning and How to Deal with It—0
Data Poisoning Attacks on Off-Policy Policy Evaluation Methods—0
Methodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilation—0
Doubly-Robust Off-Policy Evaluation with Estimated Logging Policy—0
Predictive Performance Comparison of Decision Policies Under ConfoundingCode0
Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision ProcessesCode0
Cramming Contextual Bandits for On-policy Statistical Evaluation—0
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces—0
On the Curses of Future and History in Future-dependent Value Functions for Off-policy Evaluation—0
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