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IMA Data Science Seminar - Casey Garner
Speaker: Casey Garner
Title: Cubic-Regularized Newton for Spectral Constrained Matrix Optimization and its Application to Fairness
Abstract: 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.

Oct 4, 2022 01:25 PM in Central Time (US and Canada)

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Casey Garner
Graduate Student @University of Minnesota