Transfer Learning for Large Scale Data-driven Modeling and Its Applications in Social Science

dc.contributor.advisorKhan, Latifur
dc.contributor.advisorOverzet, Lawrence
dc.contributor.committeeMemberBrandt, Patrick T.
dc.contributor.committeeMemberOuyang, Jessica J.
dc.contributor.committeeMemberDu, Ding-Zhu
dc.creatorLi, Yifan
dc.date.accessioned2023-02-22T21:04:00Z
dc.date.available2023-02-22T21:04:00Z
dc.date.created2021-12
dc.date.issued2021-12-01T06:00:00.000Z
dc.date.submittedDecember 2021
dc.date.updated2023-02-22T21:04:01Z
dc.description.abstractProliferation of Internet technology in daily life has created numerous data. These data may come from a variety of sources, such as social networks, online businesses, sensors,military surveillance and so on. Generally speaking, these data could be either dynamic or static. Such huge amount of data, along with modern powerful computing capabilities, had pushed forward tremendous amount of applications in AI substantially, including computer vision, natural language processing, cyber-security, and etc. However, most applications still depends heavily on labeled data. The need of massive labeled data hinders the progress of AI research since the labeling process is very laborious and costly, and also goes against how human beings learn - human beings do not require many labeled data by drawing inferences.Transfer learning, as a popular machine learning paradigm recently, leverages knowledge from a source domain to effectively learn predictive models in a target domain which does not have sufficient labeled data. In this dissertation, we propose to investigate transfer learning techniques on an extensive of data-driven applications. First, we unveil a heterogeneous domain adaptation framework on multiple data stream settings, and we apply it to detect cyber attacks within the data stream. Then, we show that multitask learning on multiple domains may actually help with the classification and retrieval tasks on Computer Vision(CV) applications. After the two applications, we shift the research scope to the scenario of offline learning, and test our transfer learning algorithm on sentiment classification task in Natural Language Processing (NLP) domain. We extend the application scenarios even further to the political science domain. We discuss the potential of the modern Graph Neural Networks (GNN) and apply it to the applications of time series forecasting with additional spatial information. Such application includes a number of critical tasks in social science, including but not limited to peace research, transportation analysis, and epidemic spread modeling. We further address modeling challenges observed during the aforementioned applications, such as scalability, model complexity and etc.
dc.format.mimetypeapplication/pdf
dc.identifier.uri
dc.identifier.urihttps://hdl.handle.net/10735.1/9613
dc.language.isoen
dc.subjectComputer Science
dc.subjectPolitical Science, General
dc.titleTransfer Learning for Large Scale Data-driven Modeling and Its Applications in Social Science
dc.typeThesis
dc.type.materialtext
thesis.degree.collegeSchool of Engineering and Computer Science
thesis.degree.departmentComputer Science
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.namePHD

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