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https://hdl.handle.net/2142/130042
Description
Title
Improving trustworthiness in machine learning
Author(s)
Xie, Chulin
Issue Date
2025-07-17
Director of Research (if dissertation) or Advisor (if thesis)
Li, Bo
Doctoral Committee Chair(s)
Li, Bo
Committee Member(s)
Forsyth, David
Ji, Heng
Zhao, Han
Koyejo, Sanmi
Zhang, Ce
Department of Study
Siebel School Comp & Data Sci
Discipline
Computer Science
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Machine Learning
Trustworthy
Abstract
As machine learning (ML) systems continue to scale in size and capability, concerns about their trustworthiness are increasing. This thesis addresses these challenges in the context of state-of-the-art ML systems, with a particular focus on foundation models and distributed learning. The work is organized around three interconnected pillars of trustworthy ML: privacy, robustness, and generalization. We begin by outlining the key challenges to the reliability of modern ML systems. Building on this foundation, we propose a set of methods aimed at improving the trustworthiness of ML deployment. These include methods for mitigating privacy risks through differentially private mechanisms, enhancing robustness against adversarial perturbations via certifiably robust algorithms, and identifying and addressing generalization failures. By analyzing risks and introducing mitigation strategies with theoretical guarantees, this thesis contributes scalable approaches for improving the reliability of ML systems.
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