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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 191–200 of 265 papers

TitleStatusHype
Off-Policy Evaluation of Slate Policies under Bayes Risk—0
Practical Marginalized Importance Sampling with the Successor Representation—0
Optimal Mixture Weights for Off-Policy Evaluation with Multiple Behavior Policies—0
Reliable Off-policy Evaluation for Reinforcement Learning—0
Harnessing Distribution Ratio Estimators for Learning Agents with Quality and DiversityCode0
Off-Policy Interval Estimation with Lipschitz Value Iteration—0
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment SettingsCode0
A Practical Guide of Off-Policy Evaluation for Bandit Problems—0
Off-Policy Evaluation of Bandit Algorithm from Dependent Samples under Batch Update Policy—0
CoinDICE: Off-Policy Confidence Interval Estimation—0
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