Optimization for ML and AI Seminar: Randomized linear algebra with subspace injections
Joel Tropp, Caltech Abstract: To achieve the greatest possible speed, practitioners regularly implement randomized algorithms for low-rank approximation and least-squares regression with structured dimension reduction maps. This talk outlines a new perspective on structured dimension reduction, based on the injectivity properties of the dimension reduction map. This approach provides sharper bounds for sparse dimension reduction […]