A nearly-linear time framework for graph-structured sparsity

Chinmay Hegde, Piotr Indyk, Ludwig Schmidt

    Research output: Contribution to journalConference article

    Abstract

    We introduce a framework for sparsity structures defined via graphs. Our approach is flexible and generalizes several previously studied sparsity models. Moreover, we provide efficient projection algorithms for our sparsity model that run in nearly-linear time. In the context of sparse recovery, our framework achieves an informationtheoretically optimal sample complexity for a wide range of parameters. We complement our theoretical analysis with experiments showing that our algorithms also improve on prior work in practice.

    Original languageEnglish (US)
    Pages (from-to)4165-4169
    Number of pages5
    JournalIJCAI International Joint Conference on Artificial Intelligence
    Volume2016-January
    StatePublished - 2016
    Event25th International Joint Conference on Artificial Intelligence, IJCAI 2016 - New York, United States
    Duration: Jul 9 2016Jul 15 2016

    ASJC Scopus subject areas

    • Artificial Intelligence

    Fingerprint Dive into the research topics of 'A nearly-linear time framework for graph-structured sparsity'. Together they form a unique fingerprint.

  • Cite this