PhD Thesis Proposal - Tian Ye

Title: Enhancing Adversarial Training in Low-Label Regimes  
 
 
Committee Members: Viktor Prasanna (Chair), Paul Bogdan, Jyotirmoy Deshmukh, Rajgopal Kannan, Cauligi Raghavendra  
 
 
Data & Time: April 30, 3:00 PM - 4:00 PM   Location: EEB 219  
 
 
Abstract: As machine learning models are increasingly deployed in critical real-world applications, ensuring their robustness against adversarial attacks is essential to prevent potentially severe consequences. Adversarial training, which involves teaching models to recognize and resist adversarial perturbations, is a key strategy for building such robustness. This thesis explores the enhancement of adversarial robustness in scenarios characterized by low-label regimes, where extensive labeled training data are not accessible, by addressing several challenges in existing semi-supervised adversarial training methods. Specifically, the proposed research focuses on: (1) optimizing the generation of adversarial samples to reduce the risk of overfitting, (2) enhancing the reliability of pseudo-labels to mitigate confirmation bias, and (3) simplifying the optimization of training processes to enhance accessibility and efficiency. These improvements will contribute to strengthening the security and functionality of machine learning applications against adversarial threats in a broader range of applications.

Location: Hughes Aircraft Electrical Engineering Center (EEB) - 219

Audiences: Everyone Is Invited

Contact: Ellecia Williams