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|Abstract:||Given a joint distribution P_X, Y over a space \Xcal and a label set \Ycal=\braces0, 1, we consider the problem of recovering the labels of an unlabeled sample with as few label queries as possible. The recovered labels can be passed to a passive learner, thus turning the procedure into an active learning approach. We analyze a family of labeling procedures based on a hierarchical clustering of the data. While such labeling procedures have been studied in the past, we provide a new parametrization of P_X, Y that captures their behavior in general low-noise settings, and which accounts for data-dependent clustering, thus providing new theoretical underpinning to practically used tools.|
|Citation:||Kpotufe, Samory, Ruth Urner, and Shai Ben-David. "Hierarchical Label Queries with Data-Dependent Partitions." In Proceedings of The 28th Conference on Learning Theory, PMLR 40: pp. 1176-1189. 2015.|
|Pages:||1176 - 1189|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||Proceedings of The 28th 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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