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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 51–100 of 265 papers

TitleStatusHype
An Instrumental Variable Approach to Confounded Off-Policy Evaluation—0
Off-Policy Evaluation with Online Adaptation for Robot Exploration in Challenging Environments—0
Counterfactual Analysis in Dynamic Latent State Models—0
Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation—0
CoinDICE: Off-Policy Confidence Interval Estimation—0
Characterization of Efficient Influence Function for Off-Policy Evaluation Under Optimal Policies—0
Counterfactual Learning with General Data-generating Policies—0
Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation—0
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning—0
Limit Order Book Simulation and Trade Evaluation with K-Nearest-Neighbor Resampling—0
Loss Functions for Discrete Contextual Pricing with Observational Data—0
Data-Driven Off-Policy Estimator Selection: An Application in User Marketing on An Online Content Delivery Service—0
Hyperparameter Optimization Can Even be Harmful in Off-Policy Learning and How to Deal with It—0
A Review of Off-Policy Evaluation in Reinforcement Learning—0
Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves—0
Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation—0
Designing an Interpretable Interface for Contextual Bandits—0
Accountable Off-Policy Evaluation With Kernel Bellman Statistics—0
Infinite-Horizon Offline Reinforcement Learning with Linear Function Approximation: Curse of Dimensionality and Algorithm—0
Defining Admissible Rewards for High Confidence Policy Evaluation—0
Bootstrapping Fitted Q-Evaluation for Off-Policy Inference—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
CANDOR: Counterfactual ANnotated DOubly Robust Off-Policy Evaluation—0
A Spectral Approach to Off-Policy Evaluation for POMDPs—0
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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