6 citations · 14 across the 27 of their papers we have counts for
7 papers · 1 filter
Average performance analysis of the stochastic gradient method for online PCA
Stephane Chretien, Christophe Guyeux, Zhen-Wai Olivier HO
This paper studies the complexity of the stochastic gradient algorithm for PCA when the data are observed in a streaming setting. We also propose an online approach for selecting t…
Sensing tensors with Gaussian filters
Stéphane Chrétien, Tianwen Wei
Sparse recovery from linear Gaussian measurements has been the subject of much investigation since the breaktrough papers \cite{CRT:IEEEIT06} and \cite{donoho2006compressed} on Com…
A lower bound on the expected optimal value of certain random linear programs and application to shortest paths and reliability
Stephane Chretien, Franck Corset
The paper studies the expectation of the inspection time in complex aging systems. Under reasonable assumptions, this problem is reduced to studying the expectation of the length o…
On prediction with the LASSO when the design is not incoherent
Stephane Chretien
The LASSO estimator is an -norm penalized least-squares estimator, which was introduced for variable selection in the linear model. When the design matrix satisfies, e.g. t…
On the generic uniform uniqueness of the LASSO estimator
Stephane Chretien, Sebastien Darses
The LASSO is a variable subset selection procedure in statistical linear regression based on penalization of the least-squares operator. Uniqueness of the LASSO is an impo…
Sparse recovery with unknown variance: a LASSO-type approach
Stéphane Chrétien, Sébastien Darses
We address the issue of estimating the regression vector in the generic -sparse linear model , with , , $z\sim\mathcal N(0,\sg^2 I)$ and $p…