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    Efficient preference learning for AI-powered group decision-making

    Author
    Zhao, Zhibing
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    180127_Zhao_rpi_0185E_11681.pdf (1.474Mb)
    Other Contributors
    Xia, Lirong; Zaki, Mohammed J., 1971-; Magdon-Ismail, Malik; Pazour, Jennifer A.;
    Date Issued
    2020-05
    Subject
    Computer science
    Degree
    PhD;
    Terms of Use
    This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.;
    Metadata
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    URI
    https://hdl.handle.net/20.500.13015/2544
    Abstract
    Moreover, this dissertation proposes a group decision-making framework with consists of all the three aforementioned phases: a cost-effective preference elicitation method that actively collects informative preferences from people, a composite marginal likelihood method for preference learning, and group decision prediction using randomized voting rules. Simulations demonstrate the efficacy of this framework.; Much of this dissertation focuses on the efficient learning of ranking models. The main theoretical contributions are characterizations of (non)-identifiability of mixture models under different types of data, which relate to the explainability of the learned parameter and consistent algorithms. Guided by the identifiability results, generalized-method-of-moments-based algorithms and composite marginal likelihood methods are designed and compared.; A modern group decision-making paradigm involves three phases: data collection, preference learning, and preference aggregation. All three phases can be very challenging due to the large number of alternatives and resource constraints. How can we actively collect the most informative data with low costs? How can we learn from various structures of preferences efficiently and predict unknown preferences? When can we confidently explain the learning outcome? How can we make a group decision from uncertain predictions? This dissertation aims at answering these questions.;
    Description
    May 2020; School of Science
    Department
    Dept. of Computer Science;
    Publisher
    Rensselaer Polytechnic Institute, Troy, NY
    Relationships
    Rensselaer Theses and Dissertations Online Collection;
    Access
    Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.;
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