As a result, various multiple graphical models for learning a collection of graphical models simultaneously have been derived by introducing sparsity in graphs and similarity across multiple graphs. This paper focuses on the fused multiple graphical Lasso model which encourages not only shared pattern ...
Although we focus on brain networks, symmetric graphical lasso is a tool which can be more generally applied to learn multiple networks in a context of dependent samples.doi:10.1111/rssc.12514Saverio RanciatiAlberto RoveratoAlessandra Luati
In this work, we study the Group-Fused Graphical Lasso (GFGL) which penalises partial correlations with an L1 penalty while simultaneously inducing block-wise smoothness over time to detect multiple changepoints. We present a proof of consistency for the estimator, both in terms of changepoints,...
In this work, we study the Group-Fused Graphical Lasso (GFGL) which penalises partial correlations with an L1 penalty while simultaneously inducing block-wise smoothness over time to detect multiple changepoints. We present a proof of consistency for the estimator, both in terms of changepoints,...
"Regularized Estimation of Piecewise Constant Gaussian Graphical Models: The Group-Fused Graphical Lasso." Journal of Computational and Graphical Statistics, (just-accepted).A. Gibberd and J. Nelson. Regularized estimation of piecewise constant gaussian graphical models: The group-fused graphical lasso. ...
(2017). "Large-scale Structured Sparsity via Parallel Fused Lasso on Multiple GPUs." In: Journal of Computational and Graphical Statistics to appear.Lee, Taehoon, Joong-Ho Won, Johan Lim, and Sungroh Yoon (2017). "Large-scale Structured Sparsity via Parallel Fused Lasso on Multiple GPUs." ...