(Georgia Institute of Technology, 2023-10-03)
Baker, Bradley Thomas
The enclosed research is a focused empirical and theoretical analysis of the
optimization methods in machine learning, and the underlying role that the
matrix rank of utilized learning statistics plays in these algorithms. We show
that this new perspective on machine learning optimization provides benefits in terms of communication-efficient federated learning algorithms, as well as novel insights in terms of model introspection and theory of learning dynamics. In applications to the complex domain of Neuroimaging data analysis, we show
that this rank-focused frame of reference allows for unique insights into how
models perform on particular populations.