In this paper, we propose a randomly projected convex clustering model f...
As a novel distributed learning paradigm, federated learning (FL) faces
...
Estimation of the precision matrix (or inverse covariance matrix) is of ...
Motivated by the observation that the ability of the â„“_1 norm in
promoti...
Estimation of Gaussian graphical models is important in natural science ...
Shape-constrained convex regression problem deals with fitting a convex
...
In this paper, we consider high-dimensional nonconvex square-root-loss
r...
The exclusive lasso regularization based on the â„“_1,2 norm has become
po...
Clustering is a fundamental problem in unsupervised learning. Popular me...
In this paper, we consider the problem of computing a Wasserstein baryce...
We focus on solving the clustered lasso problem, which is a least square...
Clustering may be the most fundamental problem in unsupervised learning ...
For the problems of low-rank matrix completion, the efficiency of the
wi...