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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 151–175 of 265 papers

TitleStatusHype
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation—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
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