Learning Gaussian graphical models with latent confounders

Learning Latent Variable Gaussian Graphical Models

Gaussian graphical models (GGM) have been widely used in many highdimensional applications ranging from biological and financial data to recommender systems. Sparsity in GGM plays a central role both statistically and computationally. Unfortunately, real-world data often does not fit well to sparse graphical models. In this paper, we focus on a family of latent variable Gaussian graphical model...

Learning Gaussian Graphical Models with Observed or Latent FVSs

Gaussian Graphical Models (GGMs) or Gauss Markov random fields are widelyused in many applications, and the trade-off between the modeling capacity andthe efficiency of learning and inference has been an important research prob-lem. In this paper, we study the family of GGMs with small feedback vertexsets (FVSs), where an FVS is a set of nodes whose removal breaks all the cycles...

Learning Latent Tree Graphical Models

We study the problem of learning a latent tree graphical model where samples are available only from a subset of variables. We propose two consistent and computationally efficient algorithms for learning minimal latent trees, that is, trees without any redundant hidden nodes. Unlike many existing methods, the observed nodes (or variables) are not constrained to be leaf nodes. Our algorithms can...

Learning Gaussian Graphical Models with Overlapping Blocks

We propose the GRAB framework (GRaphical models with overlApping Blocks), a novel general framework to explicitly model densely connected sub-networks in a graphical model. The GRAB learning algorithm takes as input a data matrix of p variables and n samples, and jointly learns both a network among p variables and densely connected groups of variables (called ‘blocks’). GRAB has three major nov...

Learning Gaussian Graphical Models with Overlapping Blocks