J. Salmon

发表

Gap Safe screening rules for faster complex-valued multi-task group Lasso

Alexandre Gramfort, Mathurin Massias, J. Salmon, 2017 .

Poisson noise reduction with non-local PCA

J. Salmon, C-A. Deledalle, R. Willett, 2012, ICASSP.

Adaptive Multinomial Matrix Completion

É. Moulines, J. Salmon, O. Klopp, 2014, 1408.6218.

Sharp Oracle Inequalities for Aggregation of Affine Estimators

A. Dalalyan, J. Salmon, 2011, 1104.3969.

Integer Programming on the Junction Tree Polytope for Influence Diagrams

Guillaume Obozinski, Joseph Salmon, Axel Parmentier, 2019 .

Spatially relaxed inference on high-dimensional linear models

Bertrand Thirion, Joseph Salmon, Tuan-Binh Nguyen, 2021, Statistics and Computing.

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

A. Gramfort, B. Thirion, J. Salmon, 2020, NeurIPS.

On Lasso refitting strategies

Mohamed Hebiri, J. Salmon, Evgenii Chzhen, 2017, Bernoulli.

A sharp oracle inequality for Graph-Slope

P. Bellec, J. Salmon, Samuel Vaiter, 2017, 1706.06977.

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

A. Gramfort, J. Salmon, F. Lucka, 2017, Inverse Problems.

On high dimensional regression : computational and statistical perspectives École Normale Supérieure

J. Salmon, 2018 .

Mathematical programming for influence diagrams

G. Obozinski, J. Salmon, Axel Parmentier, 2019, 1902.07039.

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

Alexandre Gramfort, Joseph Salmon, Olivier Fercoq, 2015, NIPS.

Patch reprojections for Non-Local methods

Yann Strozecki, Joseph Salmon, J. Salmon, 2012, Signal Process..

Exploiting regularity in sparse Generalized Linear Models

A. Gramfort, Mathurin Massias, J. Salmon, 2019 .

Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions pdf

Stéphan Clémençon, Igor Colin, Joseph Salmon, 2016, ICML.

Contrast re-enhancement of Total-Variation regularization jointly with the Douglas-Rachford iterations

Nicolas Papadakis, Charles-Alban Deledalle, Joseph Salmon, 2015 .

Adaptive one-bit matrix completion

É. Moulines, J. Salmon, O. Klopp, 2014 .

LassoBench: A High-Dimensional Hyperparameter

Luigi Nardi, A. Gramfort, J. Salmon, 2022 .

Benchopt: Reproducible, efficient and collaborative optimization benchmarks

A. Rakotomamonjy, Mathurin Massias, J. Salmon, 2022, 2206.13424.

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

Luigi Nardi, J. Salmon, Kenan Sehic, 2021, AutoML.

Benchopt: Reproducible, efficient and collaborative optimization benchmarks

A. Rakotomamonjy, Mathurin Massias, J. Salmon, 2022, 2206.13424.

Optimal aggregation of aﬀine estimators

A. Dalalyan, J. Salmon, 2022 .

Decoding with Conﬁdence: 1 Statistical Control on Decoder Maps

G. Varoquaux, B. Thirion, J. Salmon, 2021 .

High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent

J. Salmon, A. Bellet, M. Tommasi, 2022, ArXiv.

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

Mathieu Blondel, Mathurin Massias, J. Salmon, 2021, J. Mach. Learn. Res..

Improve learning combining crowdsourced labels by weighting Areas Under the Margin

J. Salmon, B. Charlier, Alexis Joly, 2022, ArXiv.

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

J. Salmon, Maximilien Servajean, Alexis Joly, 2022, ICML.

Benchopt: Reproducible, efficient and collaborative optimization benchmarks

Tom Dupré la Tour, Binh Duc Nguyen, A. Rakotomamonjy, 2022, NeurIPS.

High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent

J. Salmon, A. Bellet, M. Tommasi, 2022, ArXiv.

Differentially Private Coordinate Descent for Composite Empirical Risk Minimization

J. Salmon, A. Bellet, M. Tommasi, 2021, ICML.

PAC-Bayesian approach for kernel methods

E. L. Pennec, J. Salmon, 2010 .

Block-Based Refitting in 12 Sparse Regularization

N. Papadakis, J. Salmon, S. Vaiter, 2021 .

Non-local Methods with Shape-Adaptive Patches (NLM-SAP)

Charles-Alban Deledalle, Joseph Salmon, Vincent Duval, 2012, Journal of Mathematical Imaging and Vision.

Gap Safe screening rules for sparsity enforcing penalties

Alexandre Gramfort, Joseph Salmon, Olivier Fercoq, 2016, J. Mach. Learn. Res..

Learning Heteroscedastic Models by Convex Programming under Group Sparsity

Mohamed Hebiri, Arnak S. Dalalyan, Joseph Salmon, 2013, ICML.

Poisson Noise Reduction with Non-local PCA

Rebecca Willett, Charles-Alban Deledalle, Joseph Salmon, 2012, Journal of Mathematical Imaging and Vision.

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

Mathurin Massias, J. Salmon, Alexandre Gramfort, 2020, AISTATS.

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

Alexandre Gramfort, Mathurin Massias, Joseph Salmon, 2017, ArXiv.

On the benefits of output sparsity for multi-label classification pdf

Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, 2017, ArXiv.

GAP Safe Screening Rules for Sparse-Group Lasso

Alexandre Gramfort, Joseph Salmon, Olivier Fercoq, 2016, NIPS.

Optimal two-step prediction in regression

Joseph Salmon, Johannes Lederer, Didier Ch'etelat, 2014, 1410.5014.

Block-Based Refitting in $$\ell _{12}$$ ℓ 12 Sparse Regularization

N. Papadakis, J. Salmon, S. Vaiter, 2019, J. Math. Imaging Vis..

CLEAR: Covariant LEAst-Square Refitting with Applications to Image Restoration pdf

Nicolas Papadakis, Charles-Alban Deledalle, Joseph Salmon, 2016, SIAM J. Imaging Sci..

Image denoising with patch based PCA: local versus global

Arnak S. Dalalyan, Charles-Alban Deledalle, Joseph Salmon, 2011, BMVC.

From patches to pixels in Non-Local methods: Weighted-average reprojection

Yann Strozecki, Joseph Salmon, J. Salmon, 2010, 2010 IEEE International Conference on Image Processing.

Decoding with confidence: Statistical control on decoder maps

Gaël Varoquaux, Bertrand Thirion, Joseph Salmon, 2021, NeuroImage.

Heteroscedastic Concomitant Lasso for sparse multimodal electromagnetic brain imaging

Alexandre Gramfort, Mathurin Massias, Joseph Salmon, 2017 .

Oracle Inequalities and Minimax Rates for Nonlocal Means and Related Adaptive Kernel-Based Methods

Rebecca Willett, Ery Arias-Castro, Joseph Salmon, 2011, SIAM J. Imaging Sci..

Probabilistic low-rank matrix completion on finite alphabets

Eric Moulines, Joseph Salmon, Jean Lafond, 2014, NIPS.

Block based refitting in $\ell_{12}$ sparse regularisation

N. Papadakis, J. Salmon, S. Vaiter, 2019, 1910.11186.

Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence

N. Papadakis, J. Salmon, S. Vaiter, 2016, 1612.03080.

On Debiasing Restoration Algorithms: Applications to Total-Variation and Nonlocal-Means

Nicolas Papadakis, Charles-Alban Deledalle, Joseph Salmon, 2015, SSVM.

Electromagnetic neural source imaging under sparsity constraints with SURE-based hyperparameter tuning

Alexandre Gramfort, Joseph Salmon, Quentin Bertrand, 2021, 2112.12178.

Model identification and local linear convergence of coordinate descent pdf

Alexandre Gramfort, Joseph Salmon, Samuel Vaiter, 2020, ArXiv.

Implicit differentiation of Lasso-type models for hyperparameter optimization pdf

Alexandre Gramfort, Joseph Salmon, Mathieu Blondel, 2020, ICML.

Dual Extrapolation for Sparse Generalized Linear Models pdf

Alexandre Gramfort, Mathurin Massias, Joseph Salmon, 2019, ArXiv.

Celer: a Fast Solver for the Lasso with Dual Extrapolation

Alexandre Gramfort, Mathurin Massias, Joseph Salmon, 2018, ICML.

A two-head loss function for deep Average-K classification

J. Salmon, Maximilien Servajean, Camille Garcin, 2023, ArXiv.

Pl@ntNet-300K: a plant image dataset with high label ambiguity and a long-tailed distribution

J. Salmon, Maximilien Servajean, Titouan Lorieul, 2021, NeurIPS Datasets and Benchmarks.

Pl@ntNet-300K: a new plant image dataset for the evaluation of set-valued classifiers

J. Salmon, Maximilien Servajean, P. Bonnet, 2021 .

Safe Grid Search with Optimal Complexity

Ichiro Takeuchi, Tam Le, Joseph Salmon, 2018, ICML.

Alexandre Gramfort, Mathurin Massias, Joseph Salmon, 2019, NeurIPS.

Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression

Alexandre Gramfort, Mathurin Massias, Joseph Salmon, 2017, AISTATS.

Optimal mini-batch and step sizes for SAGA

Robert M. Gower, Joseph Salmon, Nidham Gazagnadou, 2019, ICML.

Pl@ntNet-300K image dataset

Maximilien Servajean, Alexis Joly, Joseph Salmon, 2021 .

Identify ambiguous tasks combining crowdsourced labels by weighting Areas Under the Margin

J. Salmon, B. Charlier, Tanguy Lefort, 2022, 2209.15380.

Mind the duality gap: safer rules for the Lasso pdf

Alexandre Gramfort, Joseph Salmon, Olivier Fercoq, 2015, ICML.

Score-Based Change Detection For Gradient-Based Learning Machines

Joseph Salmon, Zaid Harchaoui, Lang Liu, 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

A two-stage denoising filter: The preprocessed Yaroslavsky filter

Rebecca Willett, Ery Arias-Castro, Joseph Salmon, 2012, 2012 IEEE Statistical Signal Processing Workshop (SSP).

On Two Parameters for Denoising With Non-Local Means

Joseph Salmon, J. Salmon, 2010, IEEE Signal Processing Letters.

Mandatory Critical Points of 2D Uncertain Scalar Fields

Joseph Salmon, Julien Tierny, David Günther, 2014, Comput. Graph. Forum.

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

Bertrand Thirion, Alexandre Gramfort, Joseph Salmon, 2020, ArXiv.

High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent

J. Salmon, A. Bellet, M. Tommasi, 2022, AISTATS.

Refitting solutions promoted by $\ell_{12}$ sparse analysis regularization with block penalties.

Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, 2019, 1903.00741.

Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression pdf

Alexandre Gramfort, Joseph Salmon, Vincent Leclère, 2016, ArXiv.

Provably Convergent Working Set Algorithm for Non-Convex Regularized Regression

A. Rakotomamonjy, Rémi Flamary, J. Salmon, 2020, ArXiv.

Screening Rules for Lasso with Non-Convex Sparse Regularizers pdf

Alain Rakotomamonjy, Gilles Gasso, Joseph Salmon, 2019, ICML.

Screening Rules and its Complexity for Active Set Identification pdf

Joseph Salmon, Olivier Fercoq, Eugene Ndiaye, 2020, ArXiv.

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

Alexandre Gramfort, Mathurin Massias, Joseph Salmon, 2019, NeurIPS 2019.

Stable Recovery with Analysis Decomposable Priors pdf

Mohamed-Jalal Fadili, Gabriel Peyré, Charles-Alban Deledalle, 2013, ArXiv.

Extending Gossip Algorithms to Distributed Estimation of U-statistics

Stéphan Clémençon, Igor Colin, Joseph Salmon, 2015, NIPS.

Optimal aggregation of affine estimators

A. Dalalyan, J. Salmon, Joseph Salmon, 2011, COLT.

Competing against the Best Nearest Neighbor Filter in Regression

Arnak S. Dalalyan, Joseph Salmon, A. Dalalyan, 2011, ALT.

Adapting to unknown noise level in sparse deconvolution pdf

Claire Boyer, Yohann de Castro, Joseph Salmon, 2016, ArXiv.

Benchopt: Reproducible, efficient and collaborative optimization benchmarks

Tom Dupré la Tour, Binh Duc Nguyen, A. Rakotomamonjy, 2022, Neural Information Processing Systems.

Learning Heteroscedastic Models by Convex Programming under Group Sparsity

A. Dalalyan, Mohamed Hebiri, J. Salmon, 2013, ICML.

Block-Based Refitting in ℓ12\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ell _{12}$$\end{document} Sparse Regulari

N. Papadakis, J. Salmon, Charles-Alban Deledalle, 2020, Journal of Mathematical Imaging and Vision.

Local linear convergence of proximal coordinate descent algorithm

J. Salmon, Samuel Vaiter, Alexandre Gramfort, 2023, Optim. Lett..

Supervised Learning of Analysis-Sparsity Priors With Automatic Differentiation

Joseph Salmon, Samuel Vaiter, Hashem Ghanem, 2021, IEEE Signal Processing Letters.