International audienceDuring the talk we will give a necessary and sufficient condition for the uniqueness of a penalized least squares estimator whose penalty term is a polyhedral norm. Our results cover many methods including the OSCAR, SLOPE and LASSO estimators as well as the related method of basis pursuit. The geometrical condition for uniqueness involves how the row span of the design matrix intersects the faces of the dual normunit ball. Theoretical results on sparsity by LASSO and basis pursuit estimators are deduced from this condition via the characterization of accessible sign vectors for these two methods.Durant la présentation nous donnerons une condition nécessaire et suffisante pour l'unicité d'un estimateur des moindres car...
Le LASSO est une méthode de régression ajoutant à la méthode des moindres-carrés une contrainte ou...
In this thesis, we consider the linear regression model in the high dimensional setup. In particular...
Presented on September 4, 2018 from 11:00 a.m.-11:50 a.m. at the School of Mathematics, Skiles Room ...
We provide a necessary and sufficient condition for the uniqueness of penalized least-squares estima...
International audienceWe provide a necessary and sufficient condition for the uniqueness of penalize...
The lasso is a popular tool for sparse linear regression, especially for problems in which the numbe...
In this paper, we investigate the degrees of freedom ($\dof$) of penalized $\ell_1$ minimization (al...
International audienceThis paper considers the penalized least squares estimators with convex penalt...
The lasso algorithm for variable selection in linear models, introduced by Tibshirani, works by impo...
Cet article traite de la robustesse au bruit d'une régularisation polyhédrale pour la résolution de ...
The lasso algorithm for variable selection in linear models, intro- duced by Tibshirani, works by im...
In this paper, we investigate the degrees of freedom (df) of penalized l1 minimization (also known a...
To be published in 10th international conference on Sampling Theory and Applications - Full papersIn...
In this paper, we investigate the theoretical guarantees of penalized $\lun$ minimization (also call...
This paper shows that the solutions to various convex l1 minimization problems are unique if and onl...
Le LASSO est une méthode de régression ajoutant à la méthode des moindres-carrés une contrainte ou...
In this thesis, we consider the linear regression model in the high dimensional setup. In particular...
Presented on September 4, 2018 from 11:00 a.m.-11:50 a.m. at the School of Mathematics, Skiles Room ...
We provide a necessary and sufficient condition for the uniqueness of penalized least-squares estima...
International audienceWe provide a necessary and sufficient condition for the uniqueness of penalize...
The lasso is a popular tool for sparse linear regression, especially for problems in which the numbe...
In this paper, we investigate the degrees of freedom ($\dof$) of penalized $\ell_1$ minimization (al...
International audienceThis paper considers the penalized least squares estimators with convex penalt...
The lasso algorithm for variable selection in linear models, introduced by Tibshirani, works by impo...
Cet article traite de la robustesse au bruit d'une régularisation polyhédrale pour la résolution de ...
The lasso algorithm for variable selection in linear models, intro- duced by Tibshirani, works by im...
In this paper, we investigate the degrees of freedom (df) of penalized l1 minimization (also known a...
To be published in 10th international conference on Sampling Theory and Applications - Full papersIn...
In this paper, we investigate the theoretical guarantees of penalized $\lun$ minimization (also call...
This paper shows that the solutions to various convex l1 minimization problems are unique if and onl...
Le LASSO est une méthode de régression ajoutant à la méthode des moindres-carrés une contrainte ou...
In this thesis, we consider the linear regression model in the high dimensional setup. In particular...
Presented on September 4, 2018 from 11:00 a.m.-11:50 a.m. at the School of Mathematics, Skiles Room ...