Robust loss functions play a critical role in training resilient machine learning models, yet their theoretical foundations remain underexplored. In this talk, I will present my work focused on advancing the theoretical understanding of a wide range of robust loss functions commonly used in risk minimization tasks across various machine learning applications. Specifically, I will try to address the following key questions: (1) What are the primary theoretical challenges in analyzing robust loss functions? (2) How can we understand the robustness of these loss functions? (3) How can we evaluate the out-of-sample generalization performance of models trained using robust loss functions?

Biography of the speaker: Dr. Feng is an associate professor in the Department of Mathematics and Statistics at SUNY Albany. He received his Ph.D. in mathematics from the City University of Hong Kong and did his postdoc training at KU Leuven. His research focuses on machine learning theory, methods, and applications.