International audienceThis paper studies the sensitivity to the observations of the block/group Lasso solution to an overdetermined linear regression model. Such a regularization is known to promote sparsity patterns structured as nonoverlapping groups of coefficients. Our main contribution provides a local parameterization of the solution with respect to the observations. As a byproduct, we give an unbiased estimate of the degrees of freedom of the group Lasso. Among other applications of such results, one can choose in a principled and objective way the regularization parameter of the Lasso through model selection criteria
In this paper, we investigate the degrees of freedom ($\dof$) of penalized $\ell_1$ minimization (al...
International audienceIn this paper, we investigate the degrees of freedom (df) of penalized l1 mini...
International audienceIn this paper, we investigate the degrees of freedom (df) of penalized l1 mini...
International audienceThis paper studies the sensitivity to the observations of the block/group Lass...
This paper studies the sensitivity to the observations of the block/group Lasso solution to an overd...
This paper studies the sensitivity to the observations of the block/group Lasso solution to an overd...
In this paper, we are concerned with regression problems where covariates can be grouped in nonoverl...
In this paper, we are concerned with regression problems where covariates can be grouped in nonoverl...
International audienceIn this paper, we are concerned with regression problems where covariates can ...
In this paper, we are concerned with regression problems where covariates can be grouped in nonoverl...
In this paper, we are concerned with regression problems where covariates can be grouped in nonoverl...
In this paper, we investigate the degrees of freedom ($\dof$) of penalized $\ell_1$ minimization (al...
In this paper, we investigate the degrees of freedom (df) of penalized l1 minimization (also known a...
Previously entitled "The degrees of freedom of penalized l1 minimization"International audienceIn th...
Previously entitled "The degrees of freedom of penalized l1 minimization"International audienceIn th...
In this paper, we investigate the degrees of freedom ($\dof$) of penalized $\ell_1$ minimization (al...
International audienceIn this paper, we investigate the degrees of freedom (df) of penalized l1 mini...
International audienceIn this paper, we investigate the degrees of freedom (df) of penalized l1 mini...
International audienceThis paper studies the sensitivity to the observations of the block/group Lass...
This paper studies the sensitivity to the observations of the block/group Lasso solution to an overd...
This paper studies the sensitivity to the observations of the block/group Lasso solution to an overd...
In this paper, we are concerned with regression problems where covariates can be grouped in nonoverl...
In this paper, we are concerned with regression problems where covariates can be grouped in nonoverl...
International audienceIn this paper, we are concerned with regression problems where covariates can ...
In this paper, we are concerned with regression problems where covariates can be grouped in nonoverl...
In this paper, we are concerned with regression problems where covariates can be grouped in nonoverl...
In this paper, we investigate the degrees of freedom ($\dof$) of penalized $\ell_1$ minimization (al...
In this paper, we investigate the degrees of freedom (df) of penalized l1 minimization (also known a...
Previously entitled "The degrees of freedom of penalized l1 minimization"International audienceIn th...
Previously entitled "The degrees of freedom of penalized l1 minimization"International audienceIn th...
In this paper, we investigate the degrees of freedom ($\dof$) of penalized $\ell_1$ minimization (al...
International audienceIn this paper, we investigate the degrees of freedom (df) of penalized l1 mini...
International audienceIn this paper, we investigate the degrees of freedom (df) of penalized l1 mini...