Learning Sparse Gaussian Graphical Models with Overlapping Blocks Su-In Lee Department of Computer Science & Engineering, University of Washington, Seattle

We present a novel framework, called GRAB (GRaphical models with overlApping Blocks), to capture densely connected components in a network estimate. GRAB takes as input a data matrix of p variables and n samples and jointly learns both a network of the p variables and densely connected groups of variables (called'blocks'). GRAB has four major novelties as compared to existing network estimation methods: 1) It does not require blocks to be given a priori.