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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 121–130 of 265 papers

TitleStatusHype
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces—0
Off-Policy Evaluation with Online Adaptation for Robot Exploration in Challenging Environments—0
Deep Jump Q-Evaluation for Offline Policy Evaluation in Continuous Action Space—0
Limit Order Book Simulation and Trade Evaluation with K-Nearest-Neighbor Resampling—0
Data-Driven Off-Policy Estimator Selection: An Application in User Marketing on An Online Content Delivery Service—0
Logarithmic Neyman Regret for Adaptive Estimation of the Average Treatment Effect—0
Debiasing Samples from Online Learning Using Bootstrap—0
Loss Functions for Discrete Contextual Pricing with Observational Data—0
Data Poisoning Attacks on Off-Policy Policy Evaluation Methods—0
Bellman Residual Orthogonalization for Offline Reinforcement Learning—0
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