@inproceedings{2e2b58ed662a44529105dd85ad98bd17,
title = "A convex formulation for learning scale-free networks via submodular relaxation",
abstract = "A key problem in statistics and machine learning is the determination of network structure from data. We consider the case where the structure of the graph to be reconstructed is known to be scale-free. We show that in such cases it is natural to formulate structured sparsity inducing priors using submodular functions, and we use their Lov{\'a}sz extension to obtain a convex relaxation. For tractable classes such as Gaussian graphical models, this leads to a convex optimization problem that can be efficiently solved. We show that our method results in an improvement in the accuracy of reconstructed networks for synthetic data. We also show how our prior encourages scale-free reconstructions on a bioinfomatics dataset.",
author = "Defazio, {Aaron J.} and Caetano, {Tiberio S.}",
year = "2012",
language = "English",
isbn = "9781627480031",
series = "Advances in Neural Information Processing Systems",
pages = "1250--1258",
booktitle = "Advances in Neural Information Processing Systems 25",
note = "26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 ; Conference date: 03-12-2012 Through 06-12-2012",
}