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Alain Rakotomamonjy
et al. (Oct 15, 2020)

Preprint

Owing to their statistical properties, non-convex sparse regularizers have attracted much interest for estimating a sparse linear model from high dimensional data. Given that the solution is sparse, for accelerating convergence, a working set strategy addresses the optimization problem through an it...

Alain Rakotomamonjy
et al. (Oct 15, 2020)

Preprint

We address the problem of unsupervised domain adaptation under the setting of generalized target shift (both class-conditional and label shifts occur). We show that in that setting, for good generalization, it is necessary to learn with similar source and target label distributions and to match the...

Conferences and symposiums

Alain Rakotomamonjy
et al. (2019)

Conferences and symposiums

Leveraging on the convexity of the Lasso problem , screening rules help in accelerating solvers by discarding irrelevant variables, during the optimization process. However, because they provide better theoretical guarantees in identifying relevant variables, several non-convex regulariz-ers for the...

Alain Rakotomamonjy
et al. (2016)

Articles

We introduce a novel algorithm for solving learning problems where both the loss function and the regularizer are non-convex but belong to the class of difference of convex (DC) functions. Our contribution is a new general purpose proximal Newton algorithm that is able to deal with such a situation....

Alain Rakotomamonjy
et al. (2015)

Articles

This paper addresses the problem of audio scenes classification and contributes to the state of the art by proposing a novel feature. We build this feature by considering histogram of gradients (HOG) of time-frequency representation of an audio scene. Contrarily to classical audio features like MFCC...

Alain Rakotomamonjy
et al. (2011)

Articles

Recently, there has been a lot of interest around multi-task learning (MTL) problem with the constraints that tasks should share a common sparsity profile. Such a problem can be addressed through a regularization framework where the regularizer induces a joint-sparsity pattern between task decision...

Gilles Gasso
et al. (2009)

Articles

This paper considers the problem of recovering a sparse signal representation according to a signal dictionary. This problem could be formalized as a penalized least-squares problem in which sparsity is usually induced by a l1 -norm penalty on the coefficients. Such an approach known as the Lasso or...

Gilles Gasso
et al. (Aug 1, 2008)

Preprint

This paper considers the problem of recovering a sparse signal representation according to a signal dictionary. This problem is usually formalized as a penalized least-squares problem in which sparsity is usually induced by a l1 -norm penalty on the coefficient. Such an approach known as the Lasso o...

Gilles GASSO
et al. (2007)

Articles

L'article décrit le calcul des chemins de régularistion de l'algorithme v-SVR. Dans la formulation classique de cet algorithme, l'utilisateur fournit deux hyper-paramètres : v qui détermine la largeur du tube du coût ε-insensible optimisé par le SVR et le paramètre de régularisation λ qui règle le c...

Advanced Difference of Convex functions Algorithms for some topics of Machine Learning with Big Data

Thesis

. Tran Bach
(Nov 26, 2019)

Thesis

Big Data has become gradually essential and ubiquitous in all aspects nowadays. Therefore, there is an urge to develop innovative and efficient techniques to deal with the rapid growth in the volume of data. This dissertation considers the following problems in Big Data: group variable selection in...

Erik Vanhoutte
(Oct 23, 2018)

Thesis

The interest in autonomous robotics is continually expanding, especially in the domain of micro air vehicles. Indeed, much research focuses on these small-size aircraft in order to miniaturize them and to make their navigation more autonomous. This PhD thesis explores a parsimonious vision system de...

A Rakotomamonjy
et al. (Aug 18, 2016)

Preprint

Several sparsity-constrained algorithms such as Orthogonal Matching Pursuit or the Frank-Wolfe algorithm with sparsity constraints work by iteratively selecting a novel atom to add to the current non-zero set of variables. This selection step is usually performed by computing the gradient and then b...

Thi Bich Thuy Nguyen
(Dec 11, 2014)

Thesis

Image is one of the most important information in our lives. Along with the rapid development of digital image acquisition devices such as digital cameras, phone cameras, the medical imaging devices or the satellite imaging devices..., the needs of processing and analyzing images is more and more de...

François Allain
(Jul 8, 2014)

Thesis

The rapid identification of the microbial content of a complex biological sample is a major issue in biodefense and in areas related to human health, biotechnology and the environment. Tandem mass spectrometry (MS/MS) enables accurate profiling of the protein content of a sample. This thesis focuses...

Minh Thuy Ta
(Jul 4, 2014)

Thesis

This thesis focus on four problems in data mining and machine learning: clustering data streams, clustering massive data sets, weighted hard and fuzzy clustering and finally the clustering without a prior knowledge of the clusters number. Our methods are based on deterministic optimization approache...

Bilal Idiri
(Dec 17, 2013)

Thesis

The advent of positioning system technologies (AIS, radar, GPS, RFID, etc.), remote transmission (VHF, satellite, GSM, etc.), technological advances in embedded systems and low cost production, has enabled their deployment on a large scale. A huge amount of moving objects data are collected through...

Books and book chapters

Karina Zapien
(Jan 1, 2009)

Books and book chapters

Model Selection for Ranking SVM Using Regularization Path

Nicolas Delestre
et al. (2007)

Articles

Le suivi d'apprenants lors de la résolution de problèmes est difficile, surtout lorsque le nombre d'apprenants est important ou lorsque la résolution de problèmes se fait à distance. Nous proposons ici une représentation graphique en deux dimensions des traces de ces apprenants qui pourrait être uti...

Nicolas Delestre
et al. (2007)

Articles

The learner follow-up in problem solving is a hard issue. It is more difficult when there are a lot of learners or when those learners use distance learning. We propose in this paper a two-dimensional graphic representation of student’s traces. To achieve this goal, we use and modify numerical analy...