• Login
    View Item 
    •   Home
    • Theses and Dissertations
    • Dissertations
    • View Item
    •   Home
    • Theses and Dissertations
    • Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguidePlumX LibguideSubmit an Item

    Statistics

    Display statistics

    Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Thesis.pdf
    Size:
    15.59Mb
    Format:
    PDF
    Download
    Type
    Dissertation
    Authors
    Hanzely, Filip cc
    Advisors
    Richtarik, Peter cc
    Committee members
    Tempone, Raul cc
    Ghanem, Bernard cc
    Wright, Stephen
    Zhang, Tong cc
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-08-20
    Permanent link to this record
    http://hdl.handle.net/10754/664789
    
    Metadata
    Show full item record
    Abstract
    Many key problems in machine learning and data science are routinely modeled as optimization problems and solved via optimization algorithms. With the increase of the volume of data and the size and complexity of the statistical models used to formulate these often ill-conditioned optimization tasks, there is a need for new efficient algorithms able to cope with these challenges. In this thesis, we deal with each of these sources of difficulty in a different way. To efficiently address the big data issue, we develop new methods which in each iteration examine a small random subset of the training data only. To handle the big model issue, we develop methods which in each iteration update a random subset of the model parameters only. Finally, to deal with ill-conditioned problems, we devise methods that incorporate either higher-order information or Nesterov’s acceleration/momentum. In all cases, randomness is viewed as a powerful algorithmic tool that we tune, both in theory and in experiments, to achieve the best results. Our algorithms have their primary application in training supervised machine learning models via regularized empirical risk minimization, which is the dominant paradigm for training such models. However, due to their generality, our methods can be applied in many other fields, including but not limited to data science, engineering, scientific computing, and statistics.
    Citation
    Hanzely, F. (2020). Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters. KAUST Research Repository. https://doi.org/10.25781/KAUST-4F2DH
    DOI
    10.25781/KAUST-4F2DH
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-4F2DH
    Scopus Count
    Collections
    Dissertations; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2021  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.