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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 171–180 of 265 papers

TitleStatusHype
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
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