190 citations · 622 across the 19 of their papers we have counts for
3 papers · 1 filter
Learning to Discover Sparse Graphical Models
Eugene Belilovsky, Kyle Kastner, Gaël Varoquaux +1
We consider structure discovery of undirected graphical models from observational data. Inferring likely structures from few examples is a complex task often requiring the formulat…
Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity
Eugene Belilovsky, Gaël Varoquaux, Matthew B. Blaschko
Functional brain networks are well described and estimated from data with Gaussian Graphical Models (GGMs), e.g. using sparse inverse covariance estimators. Comparing functional co…
A Test of Relative Similarity For Model Selection in Generative Models
Wacha Bounliphone, Eugene Belilovsky, Matthew B. Blaschko +2
Probabilistic generative models provide a powerful framework for representing data that avoids the expense of manual annotation typically needed by discriminative approaches. Model…