Ernest Ryu, University of California, Los Angeles

Toward a grand unified theory of accelerations in optimization and machine learning
Date
Feb 27, 2025, 4:30 pm5:30 pm

Details

Event Description

Momentum-based acceleration of first-order optimization methods, first introduced by Nesterov, has been foundational to the theory and practice of large-scale optimization and machine learning. However, finding a fundamental understanding of such acceleration remains a long-standing open problem. In the past few years, several new acceleration mechanisms, distinct from Nesterov’s, have been discovered, and the similarities and dissimilarities among these new acceleration phenomena hint at a promising avenue of attack for the open problem. In this talk, we discuss the envisioned goal of developing a mathematical theory unifying the collection of acceleration mechanisms and the challenges that are to be overcome.

Short bio: Ernest Ryu is an assistant professor in the Department of Mathematics at UCLA. His current research focus is on applied mathematics, deep learning, and optimization.

Professor Ryu received a B.S. degree in Physics and Electrical engineering with honors at the California Institute of Technology in 2010 and an M.S. in Statistics and a Ph.D. in Computational and Mathematical Engineering with the Gene Golub Best Thesis Award at Stanford University in 2016. In 2016, he joined the Department of Mathematics at UCLA, as an Assistant Adjunct Professor. In 2020, he joined the Department of Mathematical Sciences at Seoul National University as a tenure-track faculty. In 2024, returned to UCLA as an assistant professor.

Event Category
Optimization Seminar