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    Exploiting structure in large-scale optimization for machine learning

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    HSIEH-DISSERTATION-2015.pdf (1.469Mb)
    Date
    2015-08
    Author
    Hsieh, Cho-Jui
    0000-0003-1624-1733
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    Abstract
    With an immense growth of data, there is a great need for solving large-scale machine learning problems. Classical optimization algorithms usually cannot scale up due to huge amount of data and/or model parameters. In this thesis, we will show that the scalability issues can often be resolved by exploiting three types of structure in machine learning problems: problem structure, model structure, and data distribution. This central idea can be applied to many machine learning problems. In this thesis, we will describe in detail how to exploit structure for kernel classification and regression, matrix factorization for recommender systems, and structure learning for graphical models. We further provide comprehensive theoretical analysis for the proposed algorithms to show both local and global convergent rate for a family of in-exact first-order and second-order optimization methods.
    Department
    Computer Sciences
    Description
    text
    Subject
    Machine learning
    Optimization
    URI
    http://hdl.handle.net/2152/31381
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