Many statistical M-estimators are based on convex optimization problems formed by the weighted sum of a loss function with a norm-based regularizer. We analyze the convergence rat...
Alekh Agarwal, Sahand Negahban, Martin J. Wainwrig...
We study the problem of learning high dimensional regression models regularized by a structured-sparsity-inducing penalty that encodes prior structural information on either input...
Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbone...
Background: The availability of various high-throughput experimental and computational methods allows biologists to rapidly infer functional relationships between genes. It is oft...
With the recent efforts made by computer vision researchers,
more and more types of features have been designed
to describe various aspects of visual characteristics.
Modeling s...
Liangliang Cao, Jiebo Luo, Feng Liang, Thomas S. H...
We consider a class of learning problems that involve a structured sparsityinducing norm defined as the sum of -norms over groups of variables. Whereas a lot of effort has been pu...