Learning Some Popular Gaussian Graphical Models without Condition Number Bounds

Understand

Gaussian Graphical Models (GGMs) have wide-ranging applications in machine learning and the natural and social sciences.

• In most of the settings in which they are applied, the number of observed samples is much smaller than the dimension and they are assumed to be sparse.
• While there are a variety of algorithms (e.g.
• Graphical Lasso, CLIME) that provably recover the graph structure with a logarithmic number of samples, they assume various conditions that require the precision matrix to be in some sense well-conditioned.