Nowadays, numerous multi-view algorithms are proposed to achieve better performance in
classification tasks. In this article, we propose a multi-view structural twin support vector …

The SLOPE estimator has the particularity of having null components (sparsity) and
components that are equal in absolute value (clustering). The number of clusters depends …

In this paper, we propose a novel safe screening test for Lasso. Our procedure is based on a
safe region with a dome geometry and exploits a canonical representation of the set of half …

We address the problem of safe screening for 1-penalized convex regression/classification
problems, ie, the identification of zero coordinates of the solutions. Unlike previous …

Tuning the regularization parameter in penalized regression models is an expensive task,
requiring multiple models to be fit along a path of parameters. Strong screening rules …

Matrix form data sets arise in many areas, so there are lots of works about the matrix
regression models. One special model of these models is the adaptive nuclear norm …

Nous présentons une nouvelle région de sûreté (safe region) pour la mise en oeuvre de
techniques d'" élimination sûre de variables"(safe screening) pour le problème LASSO. La …

Résumé L'estimateur SLOPE (acronyme signifiant≪ Sorted L One Penalized Estimation≫)
est défini comme une solution d'un probleme d'optimisation convexe ou le terme de pénalité …

Convex optimization is common in machine learning, statistics, signal, and image
processing. Solving high-dimensional optimization problems remains challenging due to …

We concern new applications of discrete geometry and combinatorics in modern statistics.
First of them focuses on the use of penalized linear regresion methods. We start our …