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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 151–200 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
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
Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation—0
Minimax Weight and Q-Function Learning for Off-Policy Evaluation—0
Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization—0
Counterfactual Mean EmbeddingsCode0
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal ModelsCode0
Cross-Validated Off-Policy EvaluationCode0
Off-Policy Evaluation Using Information Borrowing and Context-Based SwitchingCode0
Minimum Empirical Divergence for Sub-Gaussian Linear BanditsCode0
RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health InterventionsCode0
Batch Stationary Distribution EstimationCode0
Model-Free and Model-Based Policy Evaluation when Causality is UncertainCode0
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment SettingsCode0
Deeply-Debiased Off-Policy Interval EstimationCode0
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy EvaluationCode0
Control Variates for Slate Off-Policy EvaluationCode0
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided MarketsCode0
State Relevance for Off-Policy EvaluationCode0
More Robust Doubly Robust Off-policy EvaluationCode0
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