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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 126–150 of 265 papers

TitleStatusHype
STITCH-OPE: Trajectory Stitching with Guided Diffusion for Off-Policy Evaluation—0
Task Selection Policies for Multitask Learning—0
Taylor Expansion Policy Optimization—0
Cramming Contextual Bandits for On-policy Statistical Evaluation—0
The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation—0
Towards A Unified Policy Abstraction Theory and Representation Learning Approach in Markov Decision Processes—0
Towards Robust Off-Policy Evaluation via Human Inputs—0
Triply Robust Off-Policy Evaluation—0
Unbiased Offline Evaluation for Learning to Rank with Business Rules—0
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling—0
Variance-Aware Off-Policy Evaluation with Linear Function Approximation—0
Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits—0
Weighted model estimation for offline model-based reinforcement learning—0
Why Should I Trust You, Bellman? Evaluating the Bellman Objective with Off-Policy Data—0
Data-Driven Off-Policy Estimator Selection: An Application in User Marketing on An Online Content Delivery Service—0
Data Poisoning Attacks on Off-Policy Policy Evaluation Methods—0
Debiasing Samples from Online Learning Using Bootstrap—0
Deep Jump Q-Evaluation for Offline Policy Evaluation in Continuous Action Space—0
Defining Admissible Rewards for High Confidence Policy Evaluation—0
Designing an Interpretable Interface for Contextual Bandits—0
Development and Validation of Heparin Dosing Policies Using an Offline Reinforcement Learning Algorithm—0
Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learning—0
Distributional Shift-Aware Off-Policy Interval Estimation: A Unified Error Quantification Framework—0
Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation—0
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation—0
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