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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 176–200 of 265 papers

TitleStatusHype
A Practical Guide of Off-Policy Evaluation for Bandit Problems—0
A Principled Path to Fitted Distributional Evaluation—0
A Review of Off-Policy Evaluation in Reinforcement Learning—0
A Spectral Approach to Off-Policy Evaluation for POMDPs—0
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning—0
A Fast Convergence Theory for Offline Decision Making—0
A Unified Off-Policy Evaluation Approach for General Value Function—0
Automated Off-Policy Estimator Selection via Supervised Learning—0
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization—0
Balanced off-policy evaluation in general action spaces—0
Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation—0
Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation—0
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces—0
Bellman Residual Orthogonalization for Offline Reinforcement Learning—0
Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions—0
Bootstrapping Fitted Q-Evaluation for Off-Policy Inference—0
Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation—0
CANDOR: Counterfactual ANnotated DOubly Robust Off-Policy Evaluation—0
Causality and Batch Reinforcement Learning: Complementary Approaches To Planning In Unknown Domains—0
Characterization of Efficient Influence Function for Off-Policy Evaluation Under Optimal Policies—0
CoinDICE: Off-Policy Confidence Interval Estimation—0
Combining Parametric and Nonparametric Models for Off-Policy Evaluation—0
Concept-driven Off Policy Evaluation—0
Confidence Interval for Off-Policy Evaluation from Dependent Samples via Bandit Algorithm: Approach from Standardized Martingales—0
Confident Natural Policy Gradient for Local Planning in q_π-realizable Constrained MDPs—0
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