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|Abstract:||This work addresses various open questions in the theory of active learning for nonparametric classification. Our contributions are both statistical and algorithmic: \beginitemize \item We establish new minimax-rates for active learning under common noise conditions. These rates display interesting transitions – due to the interaction between noise smoothness and margin – not present in the passive setting. Some such transitions were previously conjectured, but remained unconfirmed. \item We present a generic algorithmic strategy for adaptivity to unknown noise smoothness and margin; our strategy achieves optimal rates in many general situations; furthermore, unlike in previous work, we avoid the need for adaptive confidence sets, resulting in strictly milder distributional requirements.|
|Citation:||Locatelli, Andrea, Alexandra Carpentier, and Samory Kpotufe. "Adaptivity to Noise Parameters in Nonparametric Active Learning." Proceedings of the 2017 Conference on Learning Theory, PMLR 65: pp. 1-34. 2017.|
|Pages:||1383 - 1416|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||Proceedings of the 2017 Conference on Learning Theory, PMLR|
|Version:||Final published version. Article is made available in OAR by the publisher's permission or policy.|
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