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Poster
Adaptive Estimator Selection for Off-Policy Evaluation
Yi Su · Pavithra Srinath · Akshay Krishnamurthy

Wed Jul 15 05:00 AM -- 05:45 AM & Wed Jul 15 04:00 PM -- 04:45 PM (PDT) @ None #None
in Poster Session 21 »

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.

Keywords: Recommender Systems · Reinforcement Learning · Reinforcement Learning Theory · Reinforcement Learning - Theory

Author Information

Yi Su (Cornell University)
Pavithra Srinath (Microsoft Research)
Akshay Krishnamurthy (Microsoft Research)

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