Published sanou (#21)

* moving Sanou et al to published papers

* news paper sanou

Author: jchiquet

_bibliography/in_production.bib

@@ -1,16 +0,0 @@
-@article{sanou_multiscale,
-  bibtex_show = {true},
-  author      = {Sanou, Edmond and Ambroise, Christophe and Robin, Geneviève},
-  title       = {{Inference of Multiscale Gaussian Graphical Model}},
-  journal     = {Computo},
-  year        = 2023,
-  abstract    = {Gaussian Graphical Models (GGMs) are widely used in high-dimensional data analysis to synthesize the interaction between variables. In many applications, such as genomics or image analysis, graphical models rely on sparsity and clustering to reduce dimensionality and improve performances. This paper explores a slightly different paradigm where clustering is not knowledge-driven but performed simultaneously with the graph inference task. We introduce a novel Multiscale Graphical Lasso (MGLasso) to improve networks interpretability by proposing graphs at different granularity levels. The method estimates clusters through a convex clustering approach — a relaxation of k-means, and hierarchical clustering. The conditional independence graph is simultaneously inferred through a neighborhood selection scheme for undirected graphical models. MGLasso extends and generalizes the sparse group fused lasso problem to undirected graphical models. We use continuation with Nesterov smoothing in a shrinkage-thresholding algorithm (CONESTA) to propose a regularization path of solutions along the group fused Lasso penalty, while the Lasso penalty is kept constant. Extensive experiments on synthetic data compare the performances of our model to state-of-the-art clustering methods and network inference models. Applications to gut microbiome data and poplar's methylation mixed with transcriptomic data are presented.},
-  doi =		 {10.57750/1f4p-7955},
-  repository  = {published-202306-sanou-multiscale_glasso},
-  type        = {{Research article}},
-  language    = {R and Python},
-  domain      = {Statistics},
-  keywords    = {Neighborhood selection, Convex hierarchical clustering, Gaussian graphical models},
-  issn =	 {2824-7795},  
-}
-

_bibliography/published.bib

@@ -1,4 +1,22 @@
+@article{sanou_multiscale,
+  bibtex_show = {true},
+  author      = {Sanou, Edmond and Ambroise, Christophe and Robin, Geneviève},
+  title       = {{Inference of Multiscale Gaussian Graphical Model}},
+  journal     = {Computo},
+  year        = 2023,
+  abstract    = {Gaussian Graphical Models (GGMs) are widely used in high-dimensional data analysis to synthesize the interaction between variables. In many applications, such as genomics or image analysis, graphical models rely on sparsity and clustering to reduce dimensionality and improve performances. This paper explores a slightly different paradigm where clustering is not knowledge-driven but performed simultaneously with the graph inference task. We introduce a novel Multiscale Graphical Lasso (MGLasso) to improve networks interpretability by proposing graphs at different granularity levels. The method estimates clusters through a convex clustering approach — a relaxation of k-means, and hierarchical clustering. The conditional independence graph is simultaneously inferred through a neighborhood selection scheme for undirected graphical models. MGLasso extends and generalizes the sparse group fused lasso problem to undirected graphical models. We use continuation with Nesterov smoothing in a shrinkage-thresholding algorithm (CONESTA) to propose a regularization path of solutions along the group fused Lasso penalty, while the Lasso penalty is kept constant. Extensive experiments on synthetic data compare the performances of our model to state-of-the-art clustering methods and network inference models. Applications to gut microbiome data and poplar's methylation mixed with transcriptomic data are presented.},
+  doi =		 {10.57750/1f4p-7955},
+  repository  = {published-202306-sanou-multiscale_glasso},
+  type        = {{Research article}},
+  language    = {R and Python},
+  domain      = {Statistics},
+  keywords    = {Neighborhood selection, Convex hierarchical clustering, Gaussian graphical models},
+  issn =	 {2824-7795},  
+}
+
+
 @article{chagneux_macrolitter,
+  bibtex_show =	 {true},
   author =	 {Chagneux, Mathis and Le Corff, Sylvain and 
                   Gloaguen, Pierre and Ollion, Charles and Lepâtre, Océane and
                   Bruge, Antoine},

_news/announcement_publication_202307.md

@@ -0,0 +1,7 @@
+---
+layout: post
+date: 2023-07-11 07:59:00-0400
+inline: true
+---
+
+[A new article stuck in the pipeline for a while finally published, by Edmond Sanou, Geneviève Robin and Christophe Ambroise](https://computo.sfds.asso.fr/publications/). Worth the wait: a multiscale version of Graphical-Lasso.