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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 76–100 of 265 papers

TitleStatusHype
Distributional Shift-Aware Off-Policy Interval Estimation: A Unified Error Quantification Framework—0
Causality and Batch Reinforcement Learning: Complementary Approaches To Planning In Unknown Domains—0
Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation—0
Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making—0
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation—0
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation—0
A Principled Path to Fitted Distributional Evaluation—0
Combining Parametric and Nonparametric Models for Off-Policy Evaluation—0
A Fast Convergence Theory for Offline Decision Making—0
Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions—0
Concept-driven Off Policy Evaluation—0
Doubly-Robust Off-Policy Evaluation with Estimated Logging Policy—0
Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits—0
Confident Natural Policy Gradient for Local Planning in q_π-realizable Constrained MDPs—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
Generalized Emphatic Temporal Difference Learning: Bias-Variance Analysis—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
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare—0
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