Statistical analysis for modeling dyadic interactions using machine learning methods

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dc.contributor.advisorDhillon, Inderjit S.
dc.contributor.committeeMemberGrauman, Kristen
dc.contributor.committeeMemberNiekum, Scott
dc.contributor.committeeMemberHsieh, Cho-Jui
dc.creatorChiang, Kai-Yang
dc.date.accessioned2017-06-29T14:43:31Z
dc.date.available2017-06-29T14:43:31Z
dc.date.issued2017-05
dc.date.submittedMay 2017
dc.date.updated2017-06-29T14:43:31Z
dc.description.abstractModeling dyadic interactions between entities is one of the fundamental problems in machine learning with many real-world applications, including recommender systems, data clustering, social network analysis and ranking. In this dissertation, we introduce several improved models for modeling dyadic interactions in machine learning by taking advantage of sophisticated information from different sources, such as prior structure, domain knowledge and side information. We start with exploiting different types of auxiliary information for several motivating applications, including signed link prediction, signed graph clustering, and dyadic rank aggregation. We then further move from an application-specific aspect to a general modeling aspect, where we aim to jointly exploit prior knowledge, problem structure and side information for learning low-rank modeling matrices from missing and corrupted observations. Such a modeling approach provides a general treatment to better model dyadic interactions in various machine learning applications. More importantly, we provide comprehensive theoretical analyses and performance guarantees to help us understand the utility of the additional information and quantify the merits of the proposed methods. These results therefore demonstrate the effectiveness of proposed approaches under both practical and theoretical aspects.
dc.description.departmentComputer Science
dc.format.mimetypeapplication/pdf
dc.identifierdoi:10.15781/T2R49GG93
dc.identifier.urihttp://hdl.handle.net/2152/47368
dc.subjectDyadic interaction modeling
dc.subjectStatistical machine learning
dc.subjectSigned network analysis
dc.subjectSigned graph clustering
dc.subjectDyadic rank aggregation
dc.subjectMatrix completion
dc.subjectRobust PCA
dc.subjectSide information
dc.titleStatistical analysis for modeling dyadic interactions using machine learning methods
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentComputer Sciences
thesis.degree.disciplineComputer Science
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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