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 ℓ_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 ℓ_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...