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    New formulations for active learning

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    GANTIMAHAPATRUNI-DISSERTATION-2014.pdf (1.003Mb)
    Date
    2014-01-10
    Author
    Ganti Mahapatruni, Ravi Sastry
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    Abstract
    In this thesis, we provide computationally efficient algorithms with provable statistical guarantees, for the problem of active learning, by using ideas from sequential analysis. We provide a generic algorithmic framework for active learning in the pool setting, and instantiate this framework by using ideas from learning with experts, stochastic optimization, and multi-armed bandits. For the problem of learning convex combination of a given set of hypothesis, we provide a stochastic mirror descent based active learning algorithm in the stream setting.
    URI
    http://hdl.handle.net/1853/51801
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    • Georgia Tech Theses and Dissertations [22398]
    • College of Computing Theses and Dissertations [1071]

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