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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 201–250 of 265 papers

TitleStatusHype
Conformal Off-Policy Evaluation in Markov Decision Processes—0
Conformal Off-Policy Prediction in Contextual Bandits—0
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning—0
Consistent On-Line Off-Policy Evaluation—0
Counterfactual Analysis in Dynamic Latent State Models—0
Counterfactual Learning with General Data-generating Policies—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
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation—0
Doubly robust off-policy evaluation with shrinkage—0
Doubly-Robust Off-Policy Evaluation with Estimated Logging Policy—0
Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits—0
Efficient Counterfactual Learning from Bandit Feedback—0
Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning—0
Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning—0
Efron-Stein PAC-Bayesian Inequalities—0
Emphatic TD Bellman Operator is a Contraction—0
Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment—0
Enhancing Offline Model-Based RL via Active Model Selection: A Bayesian Optimization Perspective—0
Expected Sarsa(λ) with Control Variate for Variance Reduction—0
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency—0
Generalized Emphatic Temporal Difference Learning: Bias-Variance Analysis—0
Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making—0
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare—0
Hybrid Value Estimation for Off-policy Evaluation and Offline Reinforcement Learning—0
Hyperparameter Optimization Can Even be Harmful in Off-Policy Learning and How to Deal with It—0
Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves—0
Infinite-Horizon Offline Reinforcement Learning with Linear Function Approximation: Curse of Dimensionality and Algorithm—0
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions—0
IntOPE: Off-Policy Evaluation in the Presence of Interference—0
Large-scale Validation of Counterfactual Learning Methods: A Test-Bed—0
Off-Policy Evaluation with Online Adaptation for Robot Exploration in Challenging Environments—0
Limit Order Book Simulation and Trade Evaluation with K-Nearest-Neighbor Resampling—0
Logarithmic Neyman Regret for Adaptive Estimation of the Average Treatment Effect—0
Loss Functions for Discrete Contextual Pricing with Observational Data—0
Marginalized Operators for Off-policy Reinforcement Learning—0
Markovian Interference in Experiments—0
Methodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilation—0
Minimax Value Interval for Off-Policy Evaluation and Policy Optimization—0
Minimax Model Learning—0
Minimax Off-Policy Evaluation for Multi-Armed Bandits—0
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