We propose estimating the scale parameter (mean of the eigenvalues) of t...
We consider the problem of estimating (diagonally dominant) M-matrices a...
We study the problem of estimating precision matrices in multivariate
Ga...
We propose the Terminating-Knockoff (T-Knock) filter, a fast variable
se...
We consider the problem of learning a sparse graph under Laplacian
const...
A highly popular regularized (shrinkage) covariance matrix estimator is ...
We investigate the problem of learning undirected graphical models under...
A popular regularized (shrinkage) covariance estimator is the shrinkage
...
This paper considers the problem of robustly estimating the parameters o...
Learning a graph with a specific structure is essential for interpretabi...
In this two-part work, we propose an algorithmic framework for solving
n...
The autoregressive (AR) model is a widely used model to understand time
...
Interference management is a fundamental issue in device-to-device (D2D)...
In this paper, the estimation problem for sparse reduced rank regression...
In this paper, the multiple-input multiple-output (MIMO) transmit beampa...
In econometrics and finance, the vector error correction model (VECM) is...
The problem of estimating sparse eigenvectors of a symmetric matrix attr...
This paper considers the problem of robustly estimating a structured
cov...