18 citations · 28 across the 5 of their papers we have counts for
7 papers
High-Dimensional Regularization of the Spatial Sign Covariance Matrix for Robust Shape Estimation
Jonas Elmerraji, James C. Spall, Mateo Díaz
Covariance estimation is a key component of many applications in system identification and data-driven control. Although heavy-tailed distributions may lack a covariance matrix to…
Clustering a Mixture of Gaussians with Unknown Covariance
Damek Davis, Mateo Díaz, Kaizheng Wang
We investigate a clustering problem with data from a mixture of Gaussians that share a common but unknown, and potentially ill-conditioned, covariance matrix. We start by consideri…
Escaping strict saddle points of the Moreau envelope in nonsmooth optimization
Damek Davis, Mateo Díaz, Dmitriy Drusvyatskiy
Recent work has shown that stochastically perturbed gradient methods can efficiently escape strict saddle points of smooth functions. We extend this body of work to nonsmooth optim…
Efficient Clustering for Stretched Mixtures: Landscape and Optimality
Kaizheng Wang, Yuling Yan, Mateo Díaz
This paper considers a canonical clustering problem where one receives unlabeled samples drawn from a balanced mixture of two elliptical distributions and aims for a classifier to…
Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence
Vasileios Charisopoulos, Yudong Chen, Damek Davis +3
The task of recovering a low-rank matrix from its noisy linear measurements plays a central role in computational science. Smooth formulations of the problem often exhibit an undes…
Composite optimization for robust blind deconvolution
Vasileios Charisopoulos, Damek Davis, Mateo Díaz +1
The blind deconvolution problem seeks to recover a pair of vectors from a set of rank one bilinear measurements. We consider a natural nonsmooth formulation of the problem and show…
Local angles and dimension estimation from data on manifolds
Mateo Díaz, Adolfo J. Quiroz, Mauricio Velasco
For data living in a manifold and a point we consider a statistic which estimates the variance of the angle between pairs of vectors $X…