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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…
The Lovász Hinge: A Novel Convex Surrogate for Submodular Losses
Jiaqian Yu, Matthew Blaschko
Learning with non-modular losses is an important problem when sets of predictions are made simultaneously. The main tools for constructing convex surrogate loss functions for set p…
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…