
Model identification and local linear convergence of coordinate descent
For composite nonsmooth optimization problems, ForwardBackward algorith...
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Statistical control for spatiotemporal MEG/EEG source imaging with desparsified multitask Lasso
Detecting where and when brain regions activate in a cognitive task or i...
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Screening Rules and its Complexity for Active Set Identification
Screening rules were recently introduced as a technique for explicitly i...
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Provably Convergent Working Set Algorithm for NonConvex Regularized Regression
Owing to their statistical properties, nonconvex sparse regularizers ha...
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Implicit differentiation of Lassotype models for hyperparameter optimization
Setting regularization parameters for Lassotype estimators is notorious...
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Support recovery and supnorm convergence rates for sparse pivotal estimation
In high dimensional sparse regression, pivotal estimators are estimators...
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Dual Extrapolation for Sparse Generalized Linear Models
Generalized Linear Models (GLM) form a wide class of regression and clas...
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Screening Rules for Lasso with NonConvex Sparse Regularizers
Leveraging on the convexity of the Lasso problem , screening rules help ...
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Concomitant Lasso with Repetitions (CLaR): beyond averaging multiple realizations of heteroscedastic noise
Sparsity promoting norms are frequently used in high dimensional regress...
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Optimal minibatch and step sizes for SAGA
Recently it has been shown that the step sizes of a family of variance r...
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Safe Grid Search with Optimal Complexity
Popular machine learning estimators involve regularization parameters th...
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Statistical Inference with Ensemble of Clustered Desparsified Lasso
Medical imaging involves highdimensional data, yet their acquisition is...
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A hierarchical Bayesian perspective on majorizationminimization for nonconvex sparse regression: application to M/EEG source imaging
Majorizationminimization (MM) is a standard iterative optimization tech...
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Generalized Concomitant MultiTask Lasso for sparse multimodal regression
In high dimension, it is customary to consider Lassotype estimators to ...
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From safe screening rules to working sets for faster Lassotype solvers
Convex sparsitypromoting regularizations are ubiquitous in modern stati...
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On the benefits of output sparsity for multilabel classification
The multilabel classification framework, where each observation can be ...
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Gap Safe screening rules for sparsity enforcing penalties
In high dimensional regression settings, sparsity enforcing penalties ha...
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Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression
In high dimensional settings, sparse structures are crucial for efficien...
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Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions
In decentralized networks (of sensors, connected objects, etc.), there i...
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GAP Safe Screening Rules for SparseGroupLasso
In high dimensional settings, sparse structures are crucial for efficien...
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Extending Gossip Algorithms to Distributed Estimation of UStatistics
Efficient and robust algorithms for decentralized estimation in networks...
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GAP Safe screening rules for sparse multitask and multiclass models
High dimensional regression benefits from sparsity promoting regularizat...
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Mind the duality gap: safer rules for the Lasso
Screening rules allow to early discard irrelevant variables from the opt...
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Learning Heteroscedastic Models by Convex Programming under Group Sparsity
Popular sparse estimation methods based on ℓ_1relaxation, such as the L...
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A twostage denoising filter: the preprocessed Yaroslavsky filter
This paper describes a simple image noise removal method which combines ...
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Poisson noise reduction with nonlocal PCA
Photonlimited imaging arises when the number of photons collected by a ...
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Oracle inequalities and minimax rates for nonlocal means and related adaptive kernelbased methods
This paper describes a novel theoretical characterization of the perform...
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Joseph Salmon
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Assistant Professor at TELECOM ParisTech, Associate member at INRIA Parietal, Visiting assistant professor at UW, Statistics departement.