Cubic-Regularized Newton for Spectral Constrained Matrix Optimization and its Application to Fairness
Data Science Seminar
You may attend the talk either in person in Walter 402 or register via Zoom. Registration is required to access the Zoom webinar.
Matrix functions are utilized to rewrite smooth spectral constrained matrix optimization problems as smooth unconstrained problems over the set of symmetric matrices which are then solved via the cubic-regularized Newton method. We will discuss the solution procedure and showcase our method on a new fair data science model for estimating fair and robust covariance matrices in the spirit of the Tyler's M-estimator (TME) model. This is joint work with Dr. Gilad Lerman and Dr. Shuzhong Zhang.