34 citations · 56 across the 11 of their papers we have counts for
12 papers
Variable selection in sparse GLARMA models
Marina Gomtsyan, Céline Lévy-Leduc, Sarah Ouadah +2
In this paper, we propose a novel and efficient two-stage variable selection approach for sparse GLARMA models, which are pervasive for modeling discrete-valued time series. Our ap…
An active learning approach for improving the performance of equilibrium based chemical simulations
Mary Savino, Céline Lévy-Leduc, Marc Leconte +1
In this paper, we propose a novel sequential data-driven method for dealing with equilibrium based chemical simulations, which can be seen as a specific machine learning approach c…
Variable selection in sparse GLARMA models
M. Gomtsyan, C. Lévy-Leduc, S. Ouadah +1
In this paper, we propose a novel and efficient two-stage variable selection approach for sparse GLARMA models, which are pervasive for modeling discrete-valued time series. Our ap…
A novel regularized approach for functional data clustering: An application to milking kinetics in dairy goats
C. Denis, E. Lebarbier, C. Lévy-Leduc +2
Motivated by an application to the clustering of milking kinetics of dairy goats, we propose in this paper a novel approach for functional data clustering. This issue is of growing…
Estimation of large block structured covariance matrices: Application to "multi-omic" approaches to study seed quality
Marie Perrot-Dockès, Céline Lévy-Leduc, Loïc Rajjou
Motivated by an application in high-throughput genomics and metabolomics, we propose a novel, efficient and fully data-driven approach for estimating large block structured sparse…
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…