MORE | Spring 2021
A Novel Approach to Perform Rank-One Updates in Machine Learning
The fact that ML algorithms can suffer from significant rounding errors and affect their output has received little attention. Moreover, techniques for avoiding rounding errors tend to be computationally expensive. In this research, we address both these issues by developing efficient roundoff error-free algorithms for solving the sequence of Systems of Linear Equations (SLEs) at the core of ML algorithms. Specifically, we take advantage of the fact that these SLEs are similar to each other to develop integer-preserving rank-one update algorithms that avoid having to solve the SLEs from scratch. The algorithm will be tested by performing large-scale computations via the ASU Research Computing cluster.
Venkata Saisrikar Gudivada
Hometown: Machilipatnam, Andhra Pradesh, India
Graduation date: Spring 2021