WNCG professor Aryan Mokhtari has received a grant from the National Science Foundation (NSF) to study Computationally Efficient Second-Order Optimization Algorithms for Large-Scale Learning. The project “lays out an agenda to develop a class of memory efficient, computationally affordable, and distributed friendly second-order methods for solving modern machine learning problems.”
In dense heterogeneous cellular networks, mobile devices such as smart phones can potentially associate with several different base stations. Which one should they choose? WNCG Profs.
WNCG Prof. Constantine Caramanis along with Ph.D. student Ioannis Mitliagkas and MSR Bangalore researcher Dr. Prateek Jain, have obtained the first-ever linear-memory algorithm for Principal Component Analysis. Their algorithm is efficient to implement, needs to see each data point only once, and works even in the setting of many missing entries.
In two recent papers, Caramanis, Chen, Sanghavi and Yi obtain the best known statistical and computational complexity bounds for mixed regression.