Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives
We consider the problem of learning a sparse graph underlying an undirected Gaussian
graphical model, a key problem in statistical machine learning. Given $ n $ samples from a …
graphical model, a key problem in statistical machine learning. Given $ n $ samples from a …
Does the -norm Learn a Sparse Graph under Laplacian Constrained Graphical Models?
We consider the problem of learning a sparse graph under the Laplacian constrained
Gaussian graphical models. This problem can be formulated as a penalized maximum …
Gaussian graphical models. This problem can be formulated as a penalized maximum …
Compressive Recovery of Sparse Precision Matrices
We consider the problem of learning a graph modeling the statistical relations of the $ d $
variables of a dataset with $ n $ samples $ X\in\mathbb {R}^{n\times d} $. Standard …
variables of a dataset with $ n $ samples $ X\in\mathbb {R}^{n\times d} $. Standard …
A Completely Tuning-Free and Robust Approach to Sparse Precision Matrix Estimation
Despite the vast literature on sparse Gaussian graphical models, current methods either are
asymptotically tuning-free (which still require fine-tuning in practice) or hinge on …
asymptotically tuning-free (which still require fine-tuning in practice) or hinge on …
Nonconvex sparse graph learning under Laplacian constrained graphical model
J Ying, JV de Miranda Cardoso… - Advances in Neural …, 2020 - proceedings.neurips.cc
In this paper, we consider the problem of learning a sparse graph from the Laplacian
constrained Gaussian graphical model. This problem can be formulated as a penalized …
constrained Gaussian graphical model. This problem can be formulated as a penalized …
Learning sparse graph with minimax concave penalty under Gaussian Markov random fields
This paper presents a convex-analytic framework to learn sparse graphs from data. While
our problem formulation is inspired by an extension of the graphical lasso using the so …
our problem formulation is inspired by an extension of the graphical lasso using the so …
On high-dimensional graph learning under total positivity
JK Tugnait - 2021 55th Asilomar Conference on Signals …, 2021 - ieeexplore.ieee.org
We consider the problem of estimating the structure of an undirected weighted sparse
graphical model of multivariate data under the assumption that the underlying distribution is …
graphical model of multivariate data under the assumption that the underlying distribution is …
An empirical -Wishart prior for sparse high-dimensional Gaussian graphical models
In Gaussian graphical models, the zero entries in the precision matrix determine the
dependence structure, so estimating that sparse precision matrix and, thereby, learning this …
dependence structure, so estimating that sparse precision matrix and, thereby, learning this …
Quadratic sparse gaussian graphical model estimation method for massive variables
We consider the problem of estimating a sparse Gaussian Graphical Model with a special
graph topological structure and more than a million variables. Most previous scalable …
graph topological structure and more than a million variables. Most previous scalable …
Group sparse priors for covariance estimation
Recently it has become popular to learn sparse Gaussian graphical models (GGMs) by
imposing l1 or group l1, 2 penalties on the elements of the precision matrix. Thispenalized …
imposing l1 or group l1, 2 penalties on the elements of the precision matrix. Thispenalized …