b'Joseph Salmon'

research

∙ 02/04/2022

Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification

In modern classification tasks, the number of labels is getting larger a...

Camille Garcin, et al. ∙

research

∙ 12/15/2021

Supervised learning of analysis-sparsity priors with automatic differentiation

Sparsity priors are commonly used in denoising and image reconstruction....

Hashem Ghanem, et al. ∙

research

∙ 11/04/2021

LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso

Even though Weighted Lasso regression has appealing statistical guarante...

Kenan Šehić, et al. ∙

research

∙ 10/22/2021

Differentially Private Coordinate Descent for Composite Empirical Risk Minimization

Machine learning models can leak information about the data used to trai...

Paul Mangold, et al. ∙

research

∙ 06/27/2021

Score-Based Change Detection for Gradient-Based Learning Machines

The widespread use of machine learning algorithms calls for automatic ch...

Lang Liu, et al. ∙

research

∙ 06/04/2021

Spatially relaxed inference on high-dimensional linear models

We consider the inference problem for high-dimensional linear models, wh...

Jérôme-Alexis Chevalier, et al. ∙

research

∙ 05/04/2021

Implicit differentiation for fast hyperparameter selection in non-smooth convex learning

Finding the optimal hyperparameters of a model can be cast as a bilevel ...

Quentin Bertrand, et al. ∙

research

∙ 10/22/2020

Model identification and local linear convergence of coordinate descent

For composite nonsmooth optimization problems, Forward-Backward algorith...

Quentin Klopfenstein, et al. ∙

research

∙ 09/29/2020

Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso

Detecting where and when brain regions activate in a cognitive task or i...

Jérôme-Alexis Chevalier, et al. ∙

research

∙ 09/06/2020

Screening Rules and its Complexity for Active Set Identification

Screening rules were recently introduced as a technique for explicitly i...

Eugene Ndiaye, et al. ∙

research

∙ 06/24/2020

Provably Convergent Working Set Algorithm for Non-Convex Regularized Regression

Owing to their statistical properties, non-convex sparse regularizers ha...

Alain Rakotomamonjy, et al. ∙

research

∙ 02/20/2020

Implicit differentiation of Lasso-type models for hyperparameter optimization

Setting regularization parameters for Lasso-type estimators is notorious...

Quentin Bertrand, et al. ∙

research

∙ 01/15/2020

Support recovery and sup-norm convergence rates for sparse pivotal estimation

In high dimensional sparse regression, pivotal estimators are estimators...

Mathurin Massias, et al. ∙

research

∙ 07/12/2019

Dual Extrapolation for Sparse Generalized Linear Models

Generalized Linear Models (GLM) form a wide class of regression and clas...

Mathurin Massias, et al. ∙

research

∙ 02/16/2019

Screening Rules for Lasso with Non-Convex Sparse Regularizers

Leveraging on the convexity of the Lasso problem , screening rules help ...

Alain Rakotomamonjy, et al. ∙

research

∙ 02/07/2019

Concomitant Lasso with Repetitions (CLaR): beyond averaging multiple realizations of heteroscedastic noise

Sparsity promoting norms are frequently used in high dimensional regress...

Quentin Bertrand, et al. ∙

research

∙ 01/31/2019

Optimal mini-batch and step sizes for SAGA

Recently it has been shown that the step sizes of a family of variance r...

Nidham Gazagnadou, et al. ∙

research

∙ 10/12/2018

Safe Grid Search with Optimal Complexity

Popular machine learning estimators involve regularization parameters th...

Eugene Ndiaye, et al. ∙

research

∙ 06/15/2018

Statistical Inference with Ensemble of Clustered Desparsified Lasso

Medical imaging involves high-dimensional data, yet their acquisition is...

Jérôme-Alexis Chevalier, et al. ∙

research

∙ 10/24/2017

A hierarchical Bayesian perspective on majorization-minimization for non-convex sparse regression: application to M/EEG source imaging

Majorization-minimization (MM) is a standard iterative optimization tech...

Yousra Bekhti, et al. ∙

research

∙ 05/27/2017

Generalized Concomitant Multi-Task Lasso for sparse multimodal regression

In high dimension, it is customary to consider Lasso-type estimators to ...

Mathurin Massias, et al. ∙

research

∙ 03/21/2017

From safe screening rules to working sets for faster Lasso-type solvers

Convex sparsity-promoting regularizations are ubiquitous in modern stati...

Mathurin Massias, et al. ∙

research

∙ 03/14/2017

On the benefits of output sparsity for multi-label classification

The multi-label classification framework, where each observation can be ...

Evgenii Chzhen, et al. ∙

research

∙ 11/17/2016

Gap Safe screening rules for sparsity enforcing penalties

In high dimensional regression settings, sparsity enforcing penalties ha...

Eugene Ndiaye, et al. ∙

research

∙ 06/08/2016

Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression

In high dimensional settings, sparse structures are crucial for efficien...

Eugene Ndiaye, et al. ∙

research

∙ 06/08/2016

Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions

In decentralized networks (of sensors, connected objects, etc.), there i...

Igor Colin, et al. ∙

research

∙ 02/19/2016

GAP Safe Screening Rules for Sparse-Group-Lasso

In high dimensional settings, sparse structures are crucial for efficien...

Eugene Ndiaye, et al. ∙

research

∙ 11/17/2015

Extending Gossip Algorithms to Distributed Estimation of U-Statistics

Efficient and robust algorithms for decentralized estimation in networks...

Igor Colin, et al. ∙

research

∙ 06/11/2015

GAP Safe screening rules for sparse multi-task and multi-class models

High dimensional regression benefits from sparsity promoting regularizat...

Eugene Ndiaye, et al. ∙

research

∙ 05/13/2015

Mind the duality gap: safer rules for the Lasso

Screening rules allow to early discard irrelevant variables from the opt...

Olivier Fercoq, et al. ∙

research

∙ 04/16/2013

Learning Heteroscedastic Models by Convex Programming under Group Sparsity

Popular sparse estimation methods based on ℓ_1-relaxation, such as the L...

Arnak S. Dalalyan, et al. ∙

research

∙ 08/31/2012

A two-stage denoising filter: the preprocessed Yaroslavsky filter

This paper describes a simple image noise removal method which combines ...

Joseph Salmon, et al. ∙

research

∙ 06/02/2012

Poisson noise reduction with non-local PCA

Photon-limited imaging arises when the number of photons collected by a ...

Joseph Salmon, et al. ∙

research

∙ 12/19/2011

Oracle inequalities and minimax rates for non-local means and related adaptive kernel-based methods

This paper describes a novel theoretical characterization of the perform...

Ery Arias-Castro, et al. ∙

Joseph Salmon

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