We study the problem of learning graphical models with latent variables. We give the {\em
first} efficient algorithms for learning: 1) ferromagnetic Ising models with latent variables …

Factor models have been proposed for a broad range of observed variables such as binary,
Gaussian, and variables of mixed types. They typically model a pairwise interaction …

We study the problem of learning graphical models with latent variables. We give the first
algorithm for learning locally consistent (ferromagnetic or antiferromagnetic) Restricted …

Fitting a graphical model to a collection of random variables given sample observations is a
challenging task if the observed variables are influenced by latent variables, which can …

Gaussian Graphical models (GGM) are widely used to estimate network structure in domains
ranging from biology to finance. In practice, data is often corrupted by latent confounders …

Measurement is at the core of scientific discovery. However, some quantities, such as
economic behavior or intelligence, do not allow for direct measurement. They represent …

High-dimensional statistics, which focus on datasets with a relatively large number of
variables compared to the sample size, have seen a dramatic surge of research interest and …

Network inference is becoming increasingly central in the analysis of complex phenomena
as it allows to obtain understandable models of entities interactions. Among the many …