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Adaptive Estimator Selection for Off-Policy Evaluation - MaRDI portal

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Adaptive Estimator Selection for Off-Policy Evaluation

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Publication:6335077

arXiv2002.07729MaRDI QIDQ6335077

Author name not available (Why is that?)

Publication date: 18 February 2020

Abstract: We develop a generic data-driven method for estimator selection in off-policy policy evaluation settings. We establish a strong performance guarantee for the method, showing that it is competitive with the oracle estimator, up to a constant factor. Via in-depth case studies in contextual bandits and reinforcement learning, we demonstrate the generality and applicability of the method. We also perform comprehensive experiments, demonstrating the empirical efficacy of our approach and comparing with related approaches. In both case studies, our method compares favorably with existing methods.




Has companion code repository: https://github.com/VowpalWabbit/slope-experiments








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