2 papers
cs.LG2018
Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems
Dan Garber, Atara Kaplan
Composite convex optimization problems which include both a nonsmooth term and a low-rank promoting term have important applications in machine learning and signal processing, such…
cs.LG2018
Improved Complexities of Conditional Gradient-Type Methods with Applications to Robust Matrix Recovery Problems
Dan Garber, Shoham Sabach, Atara Kaplan
Motivated by robust matrix recovery problems such as Robust Principal Component Analysis, we consider a general optimization problem of minimizing a smooth and strongly convex loss…