activity
20152020
most citedUsing the LASSO for gene selection in bladder cancer data

3 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

math.ST2020

Learning with Semi-Definite Programming: new statistical bounds based on fixed point analysis and excess risk curvature

Stéphane Chrétien, Mihai Cucuringu, Guillaume Lecué +1

Many statistical learning problems have recently been shown to be amenable to Semi-Definite Programming (SDP), with community detection and clustering in Gaussian mixture models as…

math.OC2016

On the subdifferential of symmetric convex functions of the spectrum for symmetric and orthogonally decomposable tensors

Stéphane Chrétien, Tianwen Wei

The subdifferential of convex functions of the singular spectrum of real matrices has been widely studied in matrix analysis, optimization and automatic control theory. Convex opti…

math.ST20161 cited

Small coherence implies the weak Null Space Property

Stéphane Chrétien, Zhen Wai Olivier Ho

In the Compressed Sensing community, it is well known that given a matrix with normalized columns, the Restricted Isometry Property (RIP) imp…

stat.AP20153 cited

Using the LASSO for gene selection in bladder cancer data

Stéphane Chrétien, Christophe Guyeux, Michael Boyer-Guittaut +2

Given a gene expression data array of a list of bladder cancer patients with their tumor states, it may be difficult to determine which genes can operate as disease markers when th…

stat.CO20151 cited

A Bregman Proximal ADMM for NMF with Outliers: Estimating features with missing values and outliers: a Bregman-proximal point algorithm for robust Non-negative Matrix Factorization with application to gene expression analysis

Stéphane Chrétien, Christophe Guyeux, Bastien Conesa +4

To extract the relevant features in a given dataset is a difficult task, recently resolved in the non-negative data case with the Non-negative Matrix factorization (NMF) method. Th…