NL-Means and aggregation procedures. - Joseph Salmon; Erwan Le Pennec (ICIP, 2009)

From patches to pixels in Non-Local methods: Weighted-average reprojection. - Joseph Salmon; Yann Strozecki (ICIP, 2010)

On Two Parameters for Denoising With Non-Local Means. - Joseph Salmon (IEEE Signal Process. Lett., 2010)

Competing against the Best Nearest Neighbor Filter in Regression. - Arnak S. Dalalyan; Joseph Salmon (ALT, 2011)

Image denoising with patch based PCA: local versus global. - Charles-Alban Deledalle; Joseph Salmon; Arnak S. Dalalyan (BMVC, 2011)

Anisotropic Non-Local Means with Spatially Adaptive Patch Shapes. - Charles-Alban Deledalle; Vincent Duval; Joseph Salmon (SSVM, 2011)

Oracle inequalities and minimax rates for non-local means and related adaptive kernel-based methods - Ery Arias-Castro; Joseph Salmon; Rebecca Willett (CoRR, 2011)

Optimal aggregation of affine estimators. - Joseph Salmon; Arnak S. Dalalyan (COLT, 2011)

A two-stage denoising filter: The preprocessed Yaroslavsky filter. - Joseph Salmon; Rebecca Willett; Ery Arias-Castro (SSP, 2012)

Non-local Methods with Shape-Adaptive Patches (NLM-SAP). - Charles-Alban Deledalle; Vincent Duval; Joseph Salmon (J. Math. Imaging Vis., 2012)

Oracle Inequalities and Minimax Rates for Nonlocal Means and Related Adaptive Kernel-Based Methods. - Ery Arias-Castro; Joseph Salmon; Rebecca Willett (SIAM J. Imaging Sci., 2012)

Patch reprojections for Non-Local methods. - Joseph Salmon; Yann Strozecki (Signal Process., 2012)

Learning Heteroscedastic Models by Convex Programming under Group Sparsity. - Arnak S. Dalalyan; Mohamed Hebiri; Katia Meziani; Joseph Salmon (ICML (3), 2013)

Stable Recovery with Analysis Decomposable Priors - Mohamed-Jalal Fadili; Gabriel Peyré; Samuel Vaiter; Charles-Alban Deledalle; Joseph Salmon (CoRR, 2013)

Probabilistic low-rank matrix completion on finite alphabets. - Jean Lafond; Olga Klopp; Eric Moulines; Joseph Salmon (NIPS, 2014)

Mandatory Critical Points of 2D Uncertain Scalar Fields. - David Günther; Joseph Salmon; Julien Tierny (Comput. Graph. Forum, 2014)

Poisson Noise Reduction with Non-local PCA. - Joseph Salmon; Zachary T. Harmany; Charles-Alban Deledalle; Rebecca Willett (J. Math. Imaging Vis., 2014)

Mind the duality gap: safer rules for the Lasso. - Olivier Fercoq; Alexandre Gramfort; Joseph Salmon (ICML, 2015)

Extending Gossip Algorithms to Distributed Estimation of U-statistics. - Igor Colin; Aurélien Bellet; Joseph Salmon; Stéphan Clémençon (NIPS, 2015)

GAP Safe screening rules for sparse multi-task and multi-class models. - Eugène Ndiaye; Olivier Fercoq; Alexandre Gramfort; Joseph Salmon (NIPS, 2015)

On Debiasing Restoration Algorithms: Applications to Total-Variation and Nonlocal-Means. - Charles-Alban Deledalle; Nicolas Papadakis; Joseph Salmon (SSVM, 2015)

Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions. - Igor Colin; Aurélien Bellet; Joseph Salmon; Stéphan Clémençon (ICML, 2016)

GAP Safe Screening Rules for Sparse-Group Lasso. - Eugène Ndiaye; Olivier Fercoq; Alexandre Gramfort; Joseph Salmon (NIPS, 2016)

Adapting to unknown noise level in sparse deconvolution. - Claire Boyer; Yohann de Castro; Joseph Salmon (CoRR, 2016)

CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration. - Charles-Alban Deledalle; Nicolas Papadakis; Joseph Salmon; Samuel Vaiter (CoRR, 2016)

Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression. - Eugène Ndiaye; Olivier Fercoq; Alexandre Gramfort; Vincent Leclère; Joseph Salmon (CoRR, 2016)

On the benefits of output sparsity for multi-label classification. - Evgenii Chzhen; Christophe Denis; Mohamed Hebiri; Joseph Salmon (CoRR, 2017)

From safe screening rules to working sets for faster Lasso-type solvers. - Mathurin Massias; Alexandre Gramfort; Joseph Salmon (CoRR, 2017)

Gap Safe Screening Rules for Sparsity Enforcing Penalties. - Eugène Ndiaye; Olivier Fercoq; Alexandre Gramfort; Joseph Salmon (J. Mach. Learn. Res., 2017)

CLEAR: Covariant LEAst-Square Refitting with Applications to Image Restoration. - Charles-Alban Deledalle; Nicolas Papadakis; Joseph Salmon; Samuel Vaiter (SIAM J. Imaging Sci., 2017)

Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression. - Mathurin Massias; Olivier Fercoq; Alexandre Gramfort; Joseph Salmon (AISTATS, 2018)

Celer: a Fast Solver for the Lasso with Dual Extrapolation. - Mathurin Massias; Joseph Salmon; Alexandre Gramfort (ICML, 2018)

Statistical Inference with Ensemble of Clustered Desparsified Lasso. - Jérôme-Alexis Chevalier; Joseph Salmon; Bertrand Thirion (MICCAI (1), 2018)

Optimal Mini-Batch and Step Sizes for SAGA. - Nidham Gazagnadou; Robert M. Gower; Joseph Salmon (ICML, 2019)

Safe Grid Search with Optimal Complexity. - Eugène Ndiaye; Tam Le; Olivier Fercoq; Joseph Salmon; Ichiro Takeuchi (ICML, 2019)

Screening rules for Lasso with non-convex Sparse Regularizers. - Alain Rakotomamonjy; Gilles Gasso; Joseph Salmon (ICML, 2019)

Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso. - Quentin Bertrand; Mathurin Massias; Alexandre Gramfort; Joseph Salmon (NeurIPS, 2019)

Refitting Solutions Promoted by ℓ _12 Sparse Analysis Regularizations with Block Penalties. - Charles-Alban Deledalle; Nicolas Papadakis; Joseph Salmon; Samuel Vaiter (SSVM, 2019)

Concomitant Lasso with Repetitions (CLaR): beyond averaging multiple realizations of heteroscedastic noise. - Quentin Bertrand; Mathurin Massias; Alexandre Gramfort; Joseph Salmon (CoRR, 2019)

Dual Extrapolation for Sparse Generalized Linear Models. - Mathurin Massias; Samuel Vaiter; Alexandre Gramfort; Joseph Salmon (CoRR, 2019)

Support recovery and sup-norm convergence rates for sparse pivotal estimation. - Mathurin Massias; Quentin Bertrand; Alexandre Gramfort; Joseph Salmon (AISTATS, 2020)

Implicit differentiation of Lasso-type models for hyperparameter optimization. - Quentin Bertrand; Quentin Klopfenstein; Mathieu Blondel; Samuel Vaiter; Alexandre Gramfort; Joseph Salmon (ICML, 2020)

Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso. - Jérôme-Alexis Chevalier; Joseph Salmon; Alexandre Gramfort; Bertrand Thirion (NeurIPS, 2020)

Provably Convergent Working Set Algorithm for Non-Convex Regularized Regression. - Alain Rakotomamonjy; Rémi Flamary; Gilles Gasso; Joseph Salmon (CoRR, 2020)

Screening Rules and its Complexity for Active Set Identification. - Eugène Ndiaye; Olivier Fercoq; Joseph Salmon (CoRR, 2020)

Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso. - Jérôme-Alexis Chevalier; Alexandre Gramfort; Joseph Salmon; Bertrand Thirion (CoRR, 2020)

Model identification and local linear convergence of coordinate descent. - Quentin Klopfenstein; Quentin Bertrand; Alexandre Gramfort; Joseph Salmon; Samuel Vaiter (CoRR, 2020)

Integer Programming on the Junction Tree Polytope for Influence Diagrams. - Axel Parmentier; Victor Cohen; Vincent Leclère; Guillaume Obozinski; Joseph Salmon (INFORMS J. Optim., 2020)

Elastic Net avec gestion des interactions et débiaisage. - Florent Bascou; Sophie Lèbre; Joseph Salmon (EGC, 2021)

Score-Based Change Detection For Gradient-Based Learning Machines. - Lang Liu; Joseph Salmon; Zaïd Harchaoui (ICASSP, 2021)

Pl@ntNet-300K: a plant image dataset with high label ambiguity and a long-tailed distribution. - Camille Garcin; Alexis Joly; Pierre Bonnet; Antoine Affouard; Jean-Christophe Lombardo; Mathias Chouet; Maximilien Servajean; Titouan Lorieul; Joseph Salmon (NeurIPS Datasets and Benchmarks, 2021)

Block-Based Refitting in ℓ 12 Sparse Regularization. - Charles-Alban Deledalle; Nicolas Papadakis; Joseph Salmon; Samuel Vaiter (J. Math. Imaging Vis., 2021)

Decoding with confidence: Statistical control on decoder maps. - Jérôme-Alexis Chevalier; Tuan-Binh Nguyen; Joseph Salmon; Gaël Varoquaux; Bertrand Thirion (NeuroImage, 2021)

Convergent Working Set Algorithm for Lasso with Non-Convex Sparse Regularizers. - Alain Rakotomamonjy; Rémi Flamary; Joseph Salmon; Gilles Gasso (AISTATS, 2022)

LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso. - Kenan Sehic; Alexandre Gramfort; Joseph Salmon; Luigi Nardi (AutoML, 2022)

Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification. - Camille Garcin; Maximilien Servajean; Alexis Joly; Joseph Salmon (ICML, 2022)

Differentially Private Coordinate Descent for Composite Empirical Risk Minimization. - Paul Mangold; Aurélien Bellet; Joseph Salmon; Marc Tommasi (ICML, 2022)

Benchopt: Reproducible, efficient and collaborative optimization benchmarks. - Thomas Moreau 001; Mathurin Massias; Alexandre Gramfort; Pierre Ablin; Pierre-Antoine Bannier; Benjamin Charlier; Mathieu Dagréou; Tom Dupré la Tour; Ghislain Durif; Cássio F. Dantas; Quentin Klopfenstein; Johan Larsson 002; et al. (NeurIPS, 2022)

Improve learning combining crowdsourced labels by weighting Areas Under the Margin. - Tanguy Lefort; Benjamin Charlier; Alexis Joly; Joseph Salmon (CoRR, 2022)

Implicit Differentiation for Fast Hyperparameter Selection in Non-Smooth Convex Learning. - Quentin Bertrand; Quentin Klopfenstein; Mathurin Massias; Mathieu Blondel; Samuel Vaiter; Alexandre Gramfort; Joseph Salmon (J. Mach. Learn. Res., 2022)

Spatially relaxed inference on high-dimensional linear models. - Jérôme-Alexis Chevalier; Tuan-Binh Nguyen; Bertrand Thirion; Joseph Salmon (Stat. Comput., 2022)

High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent. - Paul Mangold; Aurélien Bellet; Joseph Salmon; Marc Tommasi (AISTATS, 2023)

Supervised Learning of Analysis-Sparsity Priors With Automatic Differentiation. - Hashem Ghanem; Joseph Salmon; Nicolas Keriven; Samuel Vaiter (IEEE Signal Process. Lett., 2023)

Cooperative learning of Pl@ntNet's Artificial Intelligence algorithm: how does it work and how can we improve it? - Tanguy Lefort; Antoine Affouard; Benjamin Charlier; Jean-Christophe Lombardo; Mathias Chouet; Hervé Goëau; Joseph Salmon; Pierre Bonnet; Alexis Joly (CoRR, 2024)

Identify Ambiguous Tasks Combining Crowdsourced Labels by Weighting Areas Under the Margin. - Tanguy Lefort; Benjamin Charlier; Alexis Joly; Joseph Salmon (Trans. Mach. Learn. Res., 2024)

Local linear convergence of proximal coordinate descent algorithm. - Quentin Klopfenstein; Quentin Bertrand; Alexandre Gramfort; Joseph Salmon; Samuel Vaiter (Optim. Lett., 2024)

A Two-Head Loss Function for Deep Average-K Classification. - Camille Garcin; Maximilien Servajean; Alexis Joly; Joseph Salmon (WACV, 2025)

Conformal Prediction for Long-Tailed Classification. - Tiffany Ding; Jean-Baptiste Fermanian; Joseph Salmon (CoRR, 2025)

Class conditional conformal prediction for multiple inputs by p-value aggregation. - Jean-Baptiste Fermanian; Mohamed Hebiri; Joseph Salmon (CoRR, 2025)