Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning

Abstract

Large matrices arise in many machine learning and data analysis applications, including as representations of datasets, graphs, model weights, and first and second-order derivatives. Randomized Numerical Linear Algebra (RandNLA) is an area which uses randomness to develop improved algorithms for ubiquitous matrix problems. The area has reached a certain level of maturity; but recent hardware trends, efforts to incorporate RandNLA algorithms into core numerical libraries, and advances in machine learning, statistics, and random matrix theory, have lead to new theoretical and practical challenges. This article provides a self-contained overview of RandNLA, in light of these developments.

Locations

• arXiv (Cornell University) - View - PDF
• Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining - View