34 citations · 150 across the 18 of their papers we have counts for
19 papers
An integrated method for clustering and association network inference
Jeanne Tous, Julien Chiquet
High dimensional Gaussian graphical models provide a rigorous framework to describe a network of statistical dependencies between entities, such as genes in genomic regulation stud…
Automated calibration of consensus weighted distance-based clustering approaches using sharp
Barbara Bodinier, Dragana Vuckovic, Sabrina Rodrigues +3
In consensus clustering, a clustering algorithm is used in combination with a subsampling procedure to detect stable clusters. Previous studies on both simulated and real data sugg…
A Probabilistic Graph Coupling View of Dimension Reduction
Hugues Van Assel, Thibault Espinasse, Julien Chiquet +1
Most popular dimension reduction (DR) methods like t-SNE and UMAP are based on minimizing a cost between input and latent pairwise similarities. Though widely used, these approache…
Automated calibration for stability selection in penalised regression and graphical models
Barbara Bodinier, Sarah Filippi, Therese Haugdahl Nost +2
Stability selection represents an attractive approach to identify sparse sets of features jointly associated with an outcome in high-dimensional contexts. We introduce an automated…
Adjusting the adjusted Rand Index -- A multinomial story
Martina Sundqvist, Julien Chiquet, Guillem Rigaill
The Adjusted Rand Index () is arguably one of the most popular measures for cluster comparison. The adjustment of the is based on a hypergeometric distribution assumptio…
missSBM: An R Package for Handling Missing Values in the Stochastic Block Model
Pierre Barbillon, Julien Chiquet, Timothée Tabouy
The Stochastic Block Model (SBM) is a popular probabilistic model for random graphs. It is commonly used for clustering network data by aggregating nodes that share similar connect…
Fast Computation of Genome-Metagenome Interaction Effects
Florent Guinot, Marie Szafranski, Julien Chiquet +4
Motivation. Association studies have been widely used to search for associations between common genetic variants observations and a given phenotype. However, it is now generally ac…
Variational inference for sparse network reconstruction from count data
Julien Chiquet, Mahendra Mariadassou, Stéphane Robin
In multivariate statistics, the question of finding direct interactions can be formulated as a problem of network inference - or network reconstruction - for which the Gaussian gra…
Variable selection in multivariate linear models with high-dimensional covariance matrix estimation
Marie Perrot-Dockès, Céline Lévy-Leduc, Laure Sansonnet +1
In this paper, we propose a novel variable selection approach in the framework of multivariate linear models taking into account the dependence that may exist between the responses…
Variational Inference for Stochastic Block Models from Sampled Data
Timothée Tabouy, Pierre Barbillon, Julien Chiquet
This paper deals with non-observed dyads during the sampling of a network and consecutive issues in the inference of the Stochastic Block Model (SBM). We review sampling designs an…
Variational inference for probabilistic Poisson PCA
Julien Chiquet, Mahendra Mariadassou, Stéphane Robin
Many application domains such as ecology or genomics have to deal with multivariate non Gaussian observations. A typical example is the joint observation of the respective abundanc…
Fast Detection of Block Boundaries in Block Wise Constant Matrices: An Application to HiC data
Vincent Brault, Julien Chiquet, Céline Lévy-Leduc
We propose a novel approach for estimating the location of block boundaries (change-points) in a random matrix consisting of a block wise constant matrix observed in white noise. O…
A model for gene deregulation detection using expression data
Thomas Picchetti, Julien Chiquet, Mohamed Elati +3
In tumoral cells, gene regulation mechanisms are severely altered, and these modifications in the regulations may be characteristic of different subtypes of cancer. However, these…
Fast tree inference with weighted fusion penalties
Julien Chiquet, Pierre Gutierrez, Guillem Rigaill
Given a data set with many features observed in a large number of conditions, it is desirable to fuse and aggregate conditions which are similar to ease the interpretation and extr…
Structured Regularization for conditional Gaussian Graphical Models
Julien Chiquet, Tristan Mary-Huard, Stéphane Robin
Conditional Gaussian graphical models (cGGM) are a recent reparametrization of the multivariate linear regression model which explicitly exhibits the partial covariances betwe…
Sparsity by Worst-Case Penalties
Yves Grandvalet, Julien Chiquet, Christophe Ambroise
This paper proposes a new interpretation of sparse penalties such as the elastic-net and the group-lasso. Beyond providing a new viewpoint on these penalization schemes, our approa…
Sparsity with sign-coherent groups of variables via the cooperative-Lasso
Julien Chiquet, Yves Grandvalet, Camille Charbonnier
We consider the problems of estimation and selection of parameters endowed with a known group structure, when the groups are assumed to be sign-coherent, that is, gathering either…
Inferring Multiple Graphical Structures
Julien Chiquet, Yves Grandvalet, Christophe Ambroise
Gaussian Graphical Models provide a convenient framework for representing dependencies between variables. Recently, this tool has received a high interest for the discovery of biol…
Inferring sparse Gaussian graphical models with latent structure
Christophe Ambroise, Julien Chiquet, Catherine Matias
Our concern is selecting the concentration matrix's nonzero coefficients for a sparse Gaussian graphical model in a high-dimensional setting. This corresponds to estimating the gra…