|Date: Wednesday, March 30, 2022
Location: 4448 East Hall (4:00 PM to 5:00 PM)
Title: Concentration and Conditioning of Random Feature Matrices
Abstract: Being able to write down a mathematical model, i.e. a system of governing equations, for complex physical systems is a fundamental problem in science and engineering and is seeing assistance and automation from the machine learning perspective. As data-driven scientific discovery increases in popularity so does the need for rigorous algorithmic and theoretical work. In this talk, I will discuss a sparse random feature method with applications to learning equations from data. I will provide an overview of our theoretical results on the concentration of these random feature matrices, the connections to generalization and complexity bounds, and the design and applications of the method. Examples and applications to high-dimensional modeling and dynamics will be included.
Speaker: Hayden Schaeffer
Institution: Carnegie Mellon University
Event Organizer: Lydia Bieri firstname.lastname@example.org