Publications
Publications available on HAL, arXiv and Scholar Google.
peerannot: A framework for label aggregation in crowdsourced datasets
A. Dubar, T. Lefort and J. Salmon (2024)
JDS 2024 - 55es Journées de Statistique
[www]
Local linear convergence of proximal coordinate descent
algorithm
Q. Klopfenstein, Q. Bertrand, A. Gramfort, J. Salmon and S. Vaiter (2024)
Optimization Letter
18: (135-154).
Identify ambiguous tasks combining crowdsourced labels by weighting Areas Under the Margin
T. Lefort, B. Charlier, A. Joly and J. Salmon (2024)
TMLR
.
Peerannot: classification for crowd-sourced image datasets with Python
T. Lefort, B. Charlier, A. Joly and J. Salmon (2024)
Computo
.
[www]
Cooperative learning of Pl@ntNet's Artificial Intelligence algorithm: how does it work and how can we improve it?
T. Lefort, A. Affouard, B. Charlier, J.-C. Lombardo, M. Chouet, H. Goëau, J. Salmon, P. Bonnet and A. Joly (2024)
[www]
Weighted majority vote using Shapley values in crowdsourcing
T. Lefort, B. Charlier, A. Joly and J. Salmon (2024)
CAp 2024 - Conférence sur l'Apprentissage Automatique
[www]
Collective Intelligence and Collaborative Data Science
J. Salmon (2024)
Harvard Data Science Review
6: ().
[www]
A two-head loss function for deep Average-K classification
C. Garcin, M. Servajean, A. Joly and J. Salmon (2023)
[www]
Supervised learning of analysis-sparsity priors with automatic differentiation
H. Ghanem, J. Salmon, N. Keriven and S. Vaiter (2023)
IEEE Signal Process. Lett.
.
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent
P. Mangold, A. Bellet, J. Salmon and M. Tommasi (2023)
AISTATS
[www]
Implicit differentiation for fast hyperparameter selection in non-smooth convex learning
Q. Bertrand, Q. Klopfenstein, M. Massias, M. Blondel, S. Vaiter, A. Gramfort and J. Salmon (2022)
J. Mach. Learn. Res.
23: (1--43).
Spatially relaxed inference on high-dimensional linear models
J.-A. Chevalier, T. B. Nguyen, B. Thirion and J. Salmon (2022)
Statistics and Computing
32: (1--15).
Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification
C. Garcin, M. Servajean, A. Joly and J. Salmon (2022)
ICML
[Code]
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization
P. Mangold, A. Bellet, J. Salmon and M. Tommasi (2022)
ICML
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
T. Moreau, M. Massias, A. Gramfort, P. Ablin, B. Charlier, P.-A. Bannier, M. Dagréou, T. Dupré la Tour, G. Durif, C. F. Dantas, Q. Klopfenstein, J. Larsson, E. Lai, T. Lefort, B. Malézieux, B. Moufad, T. B. Nguyen, A. Rakotomamonjy, Z. Ramzi, J. Salmon and S. Vaiter (2022)
NeurIPS
Provably Convergent Working Set Algorithm for Non-Convex Regularized Regression
A. Rakotomamonjy, R. Flamary, G. Gasso and J. Salmon (2022)
AISTATS
LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso
K. Šehić, A. Gramfort, J. Salmon and L. Nardi (2022)
International Conference on
Automated Machine Learning
Electromagnetic neural source imaging under sparsity constraints with SURE-based hyperparameter tuning
P.-A. Bannier, Q. Bertrand, J. Salmon and A. Gramfort (2021)
Medical imaging meets NeurIPS (Workshop)
ElasticNet avec gestion des interactions et débiaisage
F. Bascou, S. Lèbre and J. Salmon (2021)
EGC
Decoding with Confidence: Statistical Control on Decoder Maps
J.-A. Chevalier, T. B. Nguyen, G. Varoquaux, J. Salmon and B. Thirion (2021)
Neuroimage
234: (117921).
Block based refitting in $\ell_12$ sparse regularisation
C.-A. Deledalle, N. Papadakis, J. Salmon and S. Vaiter (2021)
J. Math. Imaging Vis.
63: (216-236).
Pl@ntNet-300K: a plant image dataset with high label ambiguity and a long-tailed distribution
C. Garcin, A. Joly, P. Bonnet, A. Affouard, J.-C. Lombardo, M. Chouet, M. Servajean, T. Lorieul and J. Salmon (2021)
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks
Score-based change detection for gradient-based learning machines
L. Liu, J. Salmon and Z. Harchaoui (2021)
ICASSP
Screening Rules and its Complexity for Active Set Identification
E. Ndiaye, O. Fercoq and J. Salmon (2021)
J. Convex Anal.
28: (1053--1072).
Debiasing the Elastic Net for models with interactions
F. Bascou, S. Lèbre and J. Salmon (2020)
Journées de Statistique
Implicit differentiation of Lasso-type models for hyperparameter optimization
Q. Bertrand, Q. Klopfenstein, M. Blondel, S. Vaiter, A. Gramfort and J. Salmon (2020)
ICML
[Code]
Implicit differentiation of Lasso-type models for hyperparameter optimization
Q. Bertrand, Q. Klopfenstein, M. Blondel, S. Vaiter, A. Gramfort and J. Salmon (2020)
CAP
[Code]
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso
J.-A. Chevalier, A. Gramfort, J. Salmon and B. Thirion (2020)
NeurIPS
Support recovery and sup-norm convergence rates for sparse pivotal estimation
Q. Bertrand, M. Massias, A. Gramfort and J. Salmon (2020)
AISTATS
[www]
Dual extrapolation for Sparse Generalized Linear Models
M. Massias, S. Vaiter, A. Gramfort and J. Salmon (2020)
J. Mach. Learn. Res.
21: (1--33).
[Python Code] |
Integer programming on the junction tree polytope for influence diagrams
A. Parmentier, V. Cohen, V. Leclère, G. Obozinski and J. Salmon (2020)
INFORMS J. Optim.
2: (209--228).
[Code] |
Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso
Q. Bertrand, M. Massias, A. Gramfort and J. Salmon (2019)
NeurIPS
[Code]
On Lasso refitting strategies
E. Chzhen, M. Hebiri and J. Salmon (2019)
Bernoulli
25: (3175--3200).
Refitting solutions promoted by $\ell_12$ sparse analysis regularization with block penalties
C.-A. Deledalle, N. Papadakis, J. Salmon and S. Vaiter (2019)
SSVM
Optimal mini-batch and step sizes for SAGA
N. Gazagnadou, R. Gower and J. Salmon (2019)
ICML
[Code]
Score-based Change Detection for Gradient-based Learning Machines
L. Liu, J. Salmon and Z. Harchaoui (2019)
Exploiting regularity in sparse Generalized Linear Models
M. Massias, S. Vaiter, A. Gramfort and J. Salmon (2019)
SPARS
Safe Grid Search with Optimal Complexity
E. Ndiaye, T. Le, O. Fercoq, J. Salmon and I. Takeuchi (2019)
ICML
[Code]
Screening Rules for Lasso with Non-Convex Sparse Regularizers
A. Rakotomamonjy, G. Gasso and J. Salmon (2019)
ICML
[Code]
A hierarchical Bayesian perspective on majorization-minimization for non-convex sparse regression: application to M/EEG source imaging
Y. Bekhti, F. Lucka, J. Salmon and A. Gramfort (2018)
Inverse Problems
34: (085010).
[Code]
Statistical Inference with Ensemble of Clustered Desparsified Lasso
J.-A. Chevalier, J. Salmon and B. Thirion (2018)
MICCAI
On the benefits of output sparsity for multi-label classification
E. Chzhen, C. Denis, M. Hebiri and J. Salmon (2018)
Generalized Concomitant Multi-Task Lasso for sparse multimodal regression
M. Massias, O. Fercoq, A. Gramfort and J. Salmon (2018)
AISTATS
[Python Code] |
Celer: a Fast Solver for the Lasso with Dual Extrapolation
M. Massias, A. Gramfort and J. Salmon (2018)
ICML
[Python Code] |
A sharp oracle inequality for Graph-Slope
P. C. Bellec, J. Salmon and S. Vaiter (2017)
Electron. J. Statist.
11: (4851--4870).
[Code]
Adapting to unknown noise level in sparse deconvolution
C. Boyer, Y. De Castro and J. Salmon (2017)
Inf. Inference
6: (310-348).
[Demo Code] | [ZIP] |
Optimal two-step prediction in regression
D. Chételat, J. Lederer and J. Salmon (2017)
Electron. J. Statist.
11: (2519--2546).
[Python Code] |
CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration
C.-A. Deledalle, N. Papadakis, J. Salmon and S. Vaiter (2017)
SIAM J. Imaging Sci.
10: (243-284).
Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence
C.-A. Deledalle, N. Papadakis, J. Salmon and S. Vaiter (2017)
SPARS
Gap safe screening rules for faster complex-valued multi-task group Lasso
M. Massias, A. Gramfort and J. Salmon (2017)
SPARS
From safe screening rules to working sets for faster Lasso-type solvers
M. Massias, A. Gramfort and J. Salmon (2017)
NIPS-OPT
[Python Code] |
Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression
E. Ndiaye, O. Fercoq, A. Gramfort, V. Leclère and J. Salmon (2017)
Journal of Physics: Conference Series
904: (012006).
Gap Safe screening rules for sparsity enforcing penalties
E. Ndiaye, O. Fercoq, A. Gramfort and J. Salmon (2017)
J. Mach. Learn. Res.
18: (1-33).
Perspectives computationnelles et statistiques pour la régression en grande dimension
J. Salmon (2017)
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions
I. Colin, A. Bellet, J. Salmon and S. Clémençon (2016)
ICML
Un algorithme de Gossip pour l'optimisation décentralisée de fonctions sur les paires
I. Colin, A. Bellet, J. Salmon and S. Clémençon (2016)
CAP
Gap Safe Screening Rules for Sparse-Group Lasso
E. Ndiaye, O. Fercoq, A. Gramfort and J. Salmon (2016)
NeurIPS
[Code] |
Extending Gossip Algorithms to Distributed Estimation of U-Statistics
I. Colin, A. Bellet, J. Salmon and S. Clémençon (2015)
NeurIPS
[Code] |
On debiasing restoration algorithms: applications to total-variation and nonlocal-means
C.-A. Deledalle, N. Papadakis and J. Salmon (2015)
SSVM
Contrast re-enhancement of Total-Variation regularization
jointly with the Douglas-Rachford iterations
C.-A. Deledalle, N. Papadakis and J. Salmon (2015)
SPARS
Mind the duality gap: safer rules for the Lasso
O. Fercoq, A. Gramfort and J. Salmon (2015)
ICML
Règles de sélection de variables pour accélérer la localisation de sources en MEG et EEG sous contrainte de parcimonie
O. Fercoq, A. Gramfort and J. Salmon (2015)
GRETSI
Adaptive Multinomial Matrix Completion
O. Klopp, J. Lafond, E. Moulines and J. Salmon (2015)
Electron. J. Statist.
9: (2950-2975).
GAP Safe screening rules for sparse multi-task and multi-class models
E. Ndiaye, O. Fercoq, A. Gramfort and J. Salmon (2015)
NeurIPS
Mandatory Critical Points of 2D Uncertain Scalar Fields
D. Günther, J. Salmon and J. Tierny (2014)
Comput. Graph. Forum
33: (31-40).
[video] |
Probabilistic Low-rank Matrix Completion on Finite Alphabet
J. Lafond, O. Klopp, E. Moulines and J. Salmon (2014)
NeurIPS
[ZIP] |
Poisson noise reduction with non-local PCA
J. Salmon, Z. T. Harmany, C.-A. Deledalle and R. Willett (2014)
J. Math. Imaging Vis.
48: (279-294).
[Demo Matlab]
| [ZIP] |
Learning Heteroscedastic Models by Convex Programming under Group Sparsity
A. S. Dalalyan, M. Hebiri, K. Meziani and J. Salmon (2013)
ICML
Stable Recovery with Analysis Decomposable Priors
J. Fadili, G. Peyré, S. Vaiter, C.-A. Deledalle and J. Salmon (2013)
SPARS
Stable Recovery with Analysis Decomposable Priors
J. Fadili, G. Peyré, S. Vaiter, C.-A. Deledalle and J. Salmon (2013)
SampTA
Reconstruction Stable par Régularisation Décomposable Analyse
J. Fadili, G. Peyré, S. Vaiter, C.-A. Deledalle and J. Salmon (2013)
GRETSI
Oracle inequalities and minimax rates for non-local means and related adaptive kernel-based methods
E. Arias-Castro, J. Salmon and R. Willett (2012)
SIAM J. Imaging Sci.
5: (944-992).
Sharp Oracle Inequalities for Aggregation of Affine Estimators
A. S. Dalalyan and J. Salmon (2012)
Ann. Statist.
40: (2327-2355).
[Demo Matlab] | [ZIP] |
Non-local Methods with Shape-Adaptive Patches (NLM-SAP)
C.-A. Deledalle, V. Duval and J. Salmon (2012)
J. Math. Imaging Vis.
43: (103-120).
[Demo Matlab]
| [ZIP] |
Poisson Noise Reduction with Non-Local PCA
J. Salmon, C.-A. Deledalle, R. Willett and Z. T. Harmany (2012)
ICASSP
[Demo Matlab]
| [ZIP] |
Patch Reprojections for Non Local Methods
J. Salmon and Y. Strozecki (2012)
Signal Processing
92: (477 - 489).
[Demo Matlab] |
[ZIP] |
A two-stage denoising filter: the preprocessed Yaroslavsky filter
J. Salmon, R. Willett and E. Arias-Castro (2012)
SSP
[Demo Matlab] |
ZIP |
Competing against the best nearest neighbor filter in regression
A. S. Dalalyan and J. Salmon (2011)
ALT
Anisotropic Non-Local Means with Spatially Adaptive Shapes
C.-A. Deledalle, V. Duval and J. Salmon (2011)
SSVM
[Demo Matlab]
| [ZIP] |
Image denoising with patch based PCA: local versus global
C.-A. Deledalle, J. Salmon and A. S. Dalalyan (2011)
BMVC
[Demo Matlab]
| [ZIP] |
Optimal aggregation of affine estimators
J. Salmon and A. S. Dalalyan (2011)
COLT
[Demo Matlab] | [ZIP] |
On two parameters for denoising with Non-Local Means
J. Salmon (2010)
IEEE Signal Process. Lett.
17: (269-272).
From Patches to Pixels in Non-Local methods: Weighted-Average Reprojection
J. Salmon and Y. Strozecki (2010)
ICIP
[Demo Matlab] |
[ZIP] |
Agrégation d'estimateurs et méthodes à patchs pour le débruitage d'images numériques
J. Salmon (2010)
NL-Means and aggregation procedures
J. Salmon and E. Le Pennec (2009)
ICIP
An aggregator point of view on NL-Means
J. Salmon and E. Le Pennec (2009)
SPIE