Publications – Joseph Salmon

peerannot: A framework for label aggregation in crowdsourced datasets
A. Dubar, T. Lefort and J. Salmon
JDS 2024 - 55es Journées de Statistique, 2024
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Local linear convergence of proximal coordinate descent algorithm
Q. Klopfenstein, Q. Bertrand, A. Gramfort, J. Salmon and S. Vaiter
Optimization Letter, 2024
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Identify ambiguous tasks combining crowdsourced labels by weighting Areas Under the Margin
T. Lefort, B. Charlier, A. Joly and J. Salmon
TMLR, 2024
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Peerannot: classification for crowd-sourced image datasets with Python
T. Lefort, B. Charlier, A. Joly and J. Salmon
Computo, 2024
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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
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Weighted majority vote using Shapley values in crowdsourcing
T. Lefort, B. Charlier, A. Joly and J. Salmon
CAp 2024 - Conférence sur l'Apprentissage Automatique, 2024
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Collective Intelligence and Collaborative Data Science
J. Salmon
Harvard Data Science Review, 2024
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A two-head loss function for deep Average-K classification
C. Garcin, M. Servajean, A. Joly and J. Salmon
ArXiv e-prints, 2023
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Supervised learning of analysis-sparsity priors with automatic differentiation
H. Ghanem, J. Salmon, N. Keriven and S. Vaiter
IEEE Signal Process. Lett., 2023
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High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent
P. Mangold, A. Bellet, J. Salmon and M. Tommasi
AISTATS, 2023
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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
J. Mach. Learn. Res., 2022
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Spatially relaxed inference on high-dimensional linear models
J.-A. Chevalier, T. B. Nguyen, B. Thirion and J. Salmon
Statistics and Computing, 2022
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Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification
C. Garcin, M. Servajean, A. Joly and J. Salmon
ICML, 2022
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Differentially Private Coordinate Descent for Composite Empirical Risk Minimization
P. Mangold, A. Bellet, J. Salmon and M. Tommasi
ICML, 2022
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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
NeurIPS, 2022
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Provably Convergent Working Set Algorithm for Non-Convex Regularized Regression
A. Rakotomamonjy, R. Flamary, G. Gasso and J. Salmon
AISTATS, 2022
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LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso
K. Šehić, A. Gramfort, J. Salmon and L. Nardi
International Conference on Automated Machine Learning, 2022
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Electromagnetic neural source imaging under sparsity constraints with SURE-based hyperparameter tuning
P.-A. Bannier, Q. Bertrand, J. Salmon and A. Gramfort
Medical imaging meets NeurIPS (Workshop), 2021
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ElasticNet avec gestion des interactions et débiaisage
F. Bascou, S. Lèbre and J. Salmon
EGC, 2021
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Decoding with Confidence: Statistical Control on Decoder Maps
J.-A. Chevalier, T. B. Nguyen, G. Varoquaux, J. Salmon and B. Thirion
Neuroimage, 2021
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Block based refitting in $\ell_12$ sparse regularisation
C.-A. Deledalle, N. Papadakis, J. Salmon and S. Vaiter
J. Math. Imaging Vis., 2021
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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
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, 2021
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Score-based change detection for gradient-based learning machines
L. Liu, J. Salmon and Z. Harchaoui
ICASSP, 2021
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Screening Rules and its Complexity for Active Set Identification
E. Ndiaye, O. Fercoq and J. Salmon
J. Convex Anal., 2021
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Debiasing the Elastic Net for models with interactions
F. Bascou, S. Lèbre and J. Salmon
Journées de Statistique, 2020
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Implicit differentiation of Lasso-type models for hyperparameter optimization
Q. Bertrand, Q. Klopfenstein, M. Blondel, S. Vaiter, A. Gramfort and J. Salmon
ICML, 2020
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Implicit differentiation of Lasso-type models for hyperparameter optimization
Q. Bertrand, Q. Klopfenstein, M. Blondel, S. Vaiter, A. Gramfort and J. Salmon
CAP, 2020
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Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso
J.-A. Chevalier, A. Gramfort, J. Salmon and B. Thirion
NeurIPS, 2020
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Support recovery and sup-norm convergence rates for sparse pivotal estimation
Q. Bertrand, M. Massias, A. Gramfort and J. Salmon
AISTATS, 2020
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Dual extrapolation for Sparse Generalized Linear Models
M. Massias, S. Vaiter, A. Gramfort and J. Salmon
J. Mach. Learn. Res., 2020
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Integer programming on the junction tree polytope for influence diagrams
A. Parmentier, V. Cohen, V. Leclère, G. Obozinski and J. Salmon
INFORMS J. Optim., 2020
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Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso
Q. Bertrand, M. Massias, A. Gramfort and J. Salmon
NeurIPS, 2019
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On Lasso refitting strategies
E. Chzhen, M. Hebiri and J. Salmon
Bernoulli, 2019
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Refitting solutions promoted by $\ell_12$ sparse analysis regularization with block penalties
C.-A. Deledalle, N. Papadakis, J. Salmon and S. Vaiter
SSVM, 2019
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Optimal mini-batch and step sizes for SAGA
N. Gazagnadou, R. Gower and J. Salmon
ICML, 2019
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Score-based Change Detection for Gradient-based Learning Machines
L. Liu, J. Salmon and Z. Harchaoui
, 2019
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Exploiting regularity in sparse Generalized Linear Models
M. Massias, S. Vaiter, A. Gramfort and J. Salmon
SPARS, 2019
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Safe Grid Search with Optimal Complexity
E. Ndiaye, T. Le, O. Fercoq, J. Salmon and I. Takeuchi
ICML, 2019
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Screening Rules for Lasso with Non-Convex Sparse Regularizers
A. Rakotomamonjy, G. Gasso and J. Salmon
ICML, 2019
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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
Inverse Problems, 2018
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Statistical Inference with Ensemble of Clustered Desparsified Lasso
J.-A. Chevalier, J. Salmon and B. Thirion
MICCAI, 2018
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On the benefits of output sparsity for multi-label classification
E. Chzhen, C. Denis, M. Hebiri and J. Salmon
ArXiv e-prints, 2018
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Generalized Concomitant Multi-Task Lasso for sparse multimodal regression
M. Massias, O. Fercoq, A. Gramfort and J. Salmon
AISTATS, 2018
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Celer: a Fast Solver for the Lasso with Dual Extrapolation
M. Massias, A. Gramfort and J. Salmon
ICML, 2018
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A sharp oracle inequality for Graph-Slope
P. C. Bellec, J. Salmon and S. Vaiter
Electron. J. Statist., 2017
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Adapting to unknown noise level in sparse deconvolution
C. Boyer, Y. De Castro and J. Salmon
Inf. Inference, 2017
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Optimal two-step prediction in regression
D. Chételat, J. Lederer and J. Salmon
Electron. J. Statist., 2017
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CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration
C.-A. Deledalle, N. Papadakis, J. Salmon and S. Vaiter
SIAM J. Imaging Sci., 2017
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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
SPARS, 2017
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Gap safe screening rules for faster complex-valued multi-task group Lasso
M. Massias, A. Gramfort and J. Salmon
SPARS, 2017
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From safe screening rules to working sets for faster Lasso-type solvers
M. Massias, A. Gramfort and J. Salmon
NIPS-OPT, 2017
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Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression
E. Ndiaye, O. Fercoq, A. Gramfort, V. Leclère and J. Salmon
Journal of Physics: Conference Series, 2017
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Gap Safe screening rules for sparsity enforcing penalties
E. Ndiaye, O. Fercoq, A. Gramfort and J. Salmon
J. Mach. Learn. Res., 2017
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Perspectives computationnelles et statistiques pour la régression en grande dimension
J. Salmon
, 2017
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Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions
I. Colin, A. Bellet, J. Salmon and S. Clémençon
ICML, 2016
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Un algorithme de Gossip pour l'optimisation décentralisée de fonctions sur les paires
I. Colin, A. Bellet, J. Salmon and S. Clémençon
CAP, 2016
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Gap Safe Screening Rules for Sparse-Group Lasso
E. Ndiaye, O. Fercoq, A. Gramfort and J. Salmon
NeurIPS, 2016
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Extending Gossip Algorithms to Distributed Estimation of U-Statistics
I. Colin, A. Bellet, J. Salmon and S. Clémençon
NeurIPS, 2015
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On debiasing restoration algorithms: applications to total-variation and nonlocal-means
C.-A. Deledalle, N. Papadakis and J. Salmon
SSVM, 2015
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Contrast re-enhancement of Total-Variation regularization jointly with the Douglas-Rachford iterations
C.-A. Deledalle, N. Papadakis and J. Salmon
SPARS, 2015
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Mind the duality gap: safer rules for the Lasso
O. Fercoq, A. Gramfort and J. Salmon
ICML, 2015
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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
GRETSI, 2015
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Adaptive Multinomial Matrix Completion
O. Klopp, J. Lafond, E. Moulines and J. Salmon
Electron. J. Statist., 2015
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GAP Safe screening rules for sparse multi-task and multi-class models
E. Ndiaye, O. Fercoq, A. Gramfort and J. Salmon
NeurIPS, 2015
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Mandatory Critical Points of 2D Uncertain Scalar Fields
D. Günther, J. Salmon and J. Tierny
Comput. Graph. Forum, 2014
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Probabilistic Low-rank Matrix Completion on Finite Alphabet
J. Lafond, O. Klopp, E. Moulines and J. Salmon
NeurIPS, 2014
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Poisson noise reduction with non-local PCA
J. Salmon, Z. T. Harmany, C.-A. Deledalle and R. Willett
J. Math. Imaging Vis., 2014
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Learning Heteroscedastic Models by Convex Programming under Group Sparsity
A. S. Dalalyan, M. Hebiri, K. Meziani and J. Salmon
ICML, 2013
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Stable Recovery with Analysis Decomposable Priors
J. Fadili, G. Peyré, S. Vaiter, C.-A. Deledalle and J. Salmon
SPARS, 2013
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Stable Recovery with Analysis Decomposable Priors
J. Fadili, G. Peyré, S. Vaiter, C.-A. Deledalle and J. Salmon
SampTA, 2013
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Reconstruction Stable par Régularisation Décomposable Analyse
J. Fadili, G. Peyré, S. Vaiter, C.-A. Deledalle and J. Salmon
GRETSI, 2013
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Oracle inequalities and minimax rates for non-local means and related adaptive kernel-based methods
E. Arias-Castro, J. Salmon and R. Willett
SIAM J. Imaging Sci., 2012
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Sharp Oracle Inequalities for Aggregation of Affine Estimators
A. S. Dalalyan and J. Salmon
Ann. Statist., 2012
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Non-local Methods with Shape-Adaptive Patches (NLM-SAP)
C.-A. Deledalle, V. Duval and J. Salmon
J. Math. Imaging Vis., 2012
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Poisson Noise Reduction with Non-Local PCA
J. Salmon, C.-A. Deledalle, R. Willett and Z. T. Harmany
ICASSP, 2012
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Patch Reprojections for Non Local Methods
J. Salmon and Y. Strozecki
Signal Processing, 2012
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A two-stage denoising filter: the preprocessed Yaroslavsky filter
J. Salmon, R. Willett and E. Arias-Castro
SSP, 2012
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Competing against the best nearest neighbor filter in regression
A. S. Dalalyan and J. Salmon
ALT, 2011
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Anisotropic Non-Local Means with Spatially Adaptive Shapes
C.-A. Deledalle, V. Duval and J. Salmon
SSVM, 2011
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Image denoising with patch based PCA: local versus global
C.-A. Deledalle, J. Salmon and A. S. Dalalyan
BMVC, 2011
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Optimal aggregation of affine estimators
J. Salmon and A. S. Dalalyan
COLT, 2011
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On two parameters for denoising with Non-Local Means
J. Salmon
IEEE Signal Process. Lett., 2010
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From Patches to Pixels in Non-Local methods: Weighted-Average Reprojection
J. Salmon and Y. Strozecki
ICIP, 2010
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Agrégation d'estimateurs et méthodes à patchs pour le débruitage d'images numériques
J. Salmon
, 2010
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NL-Means and aggregation procedures
J. Salmon and E. Le Pennec
ICIP, 2009
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An aggregator point of view on NL-Means
J. Salmon and E. Le Pennec
Wavelets XIII, 2009
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Ondelettes et modèle Bayesien
J. Salmon
, 2007