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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 101–125 of 265 papers

TitleStatusHype
From Importance Sampling to Doubly Robust Policy GradientCode0
Future-Dependent Value-Based Off-Policy Evaluation in POMDPsCode0
Limit Order Book Simulation and Trade Evaluation with K-Nearest-Neighbor Resampling—0
Data-Driven Off-Policy Estimator Selection: An Application in User Marketing on An Online Content Delivery Service—0
Off-Policy Evaluation with Online Adaptation for Robot Exploration in Challenging Environments—0
Large-scale Validation of Counterfactual Learning Methods: A Test-Bed—0
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces—0
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions—0
Counterfactual Learning with General Data-generating Policies—0
Infinite-Horizon Offline Reinforcement Learning with Linear Function Approximation: Curse of Dimensionality and Algorithm—0
Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves—0
Hyperparameter Optimization Can Even be Harmful in Off-Policy Learning and How to Deal with It—0
Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation—0
Adaptive Trade-Offs in Off-Policy Learning—0
Hybrid Value Estimation for Off-policy Evaluation and Offline Reinforcement Learning—0
IntOPE: Off-Policy Evaluation in the Presence of Interference—0
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare—0
Counterfactual Analysis in Dynamic Latent State Models—0
Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making—0
Generalized Emphatic Temporal Difference Learning: Bias-Variance Analysis—0
Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation—0
Consistent On-Line Off-Policy Evaluation—0
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency—0
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning—0
Expected Sarsa(λ) with Control Variate for Variance Reduction—0
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