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Hierarchical label queries with data-dependent partitions

Author(s): Kpotufe, S; Urner, R; Ben-David, S

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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.
Publication Date: 2015
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.
ISSN: 2640-3498
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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