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2020 – today
- 2024
[j16]Quentin Klopfenstein
, Quentin Bertrand, Alexandre Gramfort, Joseph Salmon, Samuel Vaiter:
Local linear convergence of proximal coordinate descent algorithm. Optim. Lett. 18(1): 135-154 (2024)- [j15]Tanguy Lefort, Benjamin Charlier, Alexis Joly, Joseph Salmon:
Identify Ambiguous Tasks Combining Crowdsourced Labels by Weighting Areas Under the Margin. Trans. Mach. Learn. Res. 2024 (2024) - [i36]Tanguy Lefort, Antoine Affouard, Benjamin Charlier, Jean-Christophe Lombardo, Mathias Chouet, Hervé Goëau, Joseph Salmon, Pierre Bonnet, Alexis Joly:
Cooperative learning of Pl@ntNet's Artificial Intelligence algorithm: how does it work and how can we improve it? CoRR abs/2406.03356 (2024) - 2023
- [j14]Hashem Ghanem, Joseph Salmon, Nicolas Keriven, Samuel Vaiter:
Supervised Learning of Analysis-Sparsity Priors With Automatic Differentiation. IEEE Signal Process. Lett. 30: 339-343 (2023) - [c36]Paul Mangold, Aurélien Bellet, Joseph Salmon, Marc Tommasi:
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent. AISTATS 2023: 4894-4916 - [i35]Camille Garcin, Maximilien Servajean, Alexis Joly, Joseph Salmon:
A two-head loss function for deep Average-K classification. CoRR abs/2303.18118 (2023) - 2022
- [j13]Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon:
Implicit Differentiation for Fast Hyperparameter Selection in Non-Smooth Convex Learning. J. Mach. Learn. Res. 23: 149:1-149:43 (2022) - [j12]Jérôme-Alexis Chevalier, Tuan-Binh Nguyen, Bertrand Thirion, Joseph Salmon:
Spatially relaxed inference on high-dimensional linear models. Stat. Comput. 32(5): 83 (2022) - [c35]Alain Rakotomamonjy, Rémi Flamary, Joseph Salmon, Gilles Gasso:
Convergent Working Set Algorithm for Lasso with Non-Convex Sparse Regularizers. AISTATS 2022: 5196-5211 - [c34]Kenan Sehic, Alexandre Gramfort, Joseph Salmon, Luigi Nardi:
LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso. AutoML 2022: 2/1-24 - [c33]Camille Garcin, Maximilien Servajean, Alexis Joly, Joseph Salmon:
Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification. ICML 2022: 7208-7222 - [c32]Paul Mangold, Aurélien Bellet, Joseph Salmon, Marc Tommasi:
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization. ICML 2022: 14948-14978 - [c31]Thomas Moreau, 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, En Lai, Tanguy Lefort, Benoît Malézieux, Badr Moufad, Binh T. Nguyen, Alain Rakotomamonjy, Zaccharie Ramzi, Joseph Salmon, Samuel Vaiter:
Benchopt: Reproducible, efficient and collaborative optimization benchmarks. NeurIPS 2022 - [i34]Camille Garcin, Maximilien Servajean, Alexis Joly, Joseph Salmon:
Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification. CoRR abs/2202.02193 (2022) - [i33]Thomas Moreau, 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, En Lai, Tanguy Lefort, Benoît Malézieux, Badr Moufad, Binh T. Nguyen, Alain Rakotomamonjy, Zaccharie Ramzi, Joseph Salmon, Samuel Vaiter:
Benchopt: Reproducible, efficient and collaborative optimization benchmarks. CoRR abs/2206.13424 (2022) - [i32]Paul Mangold, Aurélien Bellet, Joseph Salmon, Marc Tommasi:
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent. CoRR abs/2207.01560 (2022) - [i31]Tanguy Lefort, Benjamin Charlier, Alexis Joly, Joseph Salmon:
Improve learning combining crowdsourced labels by weighting Areas Under the Margin. CoRR abs/2209.15380 (2022) - 2021
- [j11]Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter:
Block-Based Refitting in ℓ 12 Sparse Regularization. J. Math. Imaging Vis. 63(2): 216-236 (2021) - [j10]Jérôme-Alexis Chevalier, Tuan-Binh Nguyen, Joseph Salmon, Gaël Varoquaux, Bertrand Thirion:
Decoding with confidence: Statistical control on decoder maps. NeuroImage 234: 117921 (2021) - [c30]Florent Bascou, Sophie Lèbre, Joseph Salmon:
Elastic Net avec gestion des interactions et débiaisage. EGC 2021: 269-276 - [c29]Lang Liu, Joseph Salmon, Zaïd Harchaoui:
Score-Based Change Detection For Gradient-Based Learning Machines. ICASSP 2021: 4990-4994 - [c28]Camille Garcin, Alexis Joly, Pierre Bonnet, Antoine Affouard, Jean-Christophe Lombardo, Mathias Chouet, Maximilien Servajean, Titouan Lorieul, Joseph Salmon:
Pl@ntNet-300K: a plant image dataset with high label ambiguity and a long-tailed distribution. NeurIPS Datasets and Benchmarks 2021 - [i30]Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon:
Implicit differentiation for fast hyperparameter selection in non-smooth convex learning. CoRR abs/2105.01637 (2021) - [i29]Lang Liu, Joseph Salmon, Zaïd Harchaoui:
Score-Based Change Detection for Gradient-Based Learning Machines. CoRR abs/2106.14122 (2021) - [i28]Paul Mangold, Aurélien Bellet, Joseph Salmon, Marc Tommasi:
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization. CoRR abs/2110.11688 (2021) - [i27]Kenan Sehic, Alexandre Gramfort, Joseph Salmon, Luigi Nardi:
LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso. CoRR abs/2111.02790 (2021) - 2020
- [j9]Axel Parmentier, Victor Cohen, Vincent Leclère, Guillaume Obozinski, Joseph Salmon:
Integer Programming on the Junction Tree Polytope for Influence Diagrams. INFORMS J. Optim. 2(3): 209-228 (2020) - [c27]Mathurin Massias, Quentin Bertrand, Alexandre Gramfort, Joseph Salmon:
Support recovery and sup-norm convergence rates for sparse pivotal estimation. AISTATS 2020: 2655-2665 - [c26]Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon:
Implicit differentiation of Lasso-type models for hyperparameter optimization. ICML 2020: 810-821 - [c25]Jérôme-Alexis Chevalier, Joseph Salmon, Alexandre Gramfort, Bertrand Thirion:
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso. NeurIPS 2020 - [i26]Mathurin Massias, Quentin Bertrand, Alexandre Gramfort, Joseph Salmon:
Support recovery and sup-norm convergence rates for sparse pivotal estimation. CoRR abs/2001.05401 (2020) - [i25]Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon:
Implicit differentiation of Lasso-type models for hyperparameter optimization. CoRR abs/2002.08943 (2020) - [i24]Alain Rakotomamonjy, Rémi Flamary, Gilles Gasso, Joseph Salmon:
Provably Convergent Working Set Algorithm for Non-Convex Regularized Regression. CoRR abs/2006.13533 (2020) - [i23]Eugène Ndiaye, Olivier Fercoq, Joseph Salmon:
Screening Rules and its Complexity for Active Set Identification. CoRR abs/2009.02709 (2020) - [i22]Jérôme-Alexis Chevalier, Alexandre Gramfort, Joseph Salmon, Bertrand Thirion:
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso. CoRR abs/2009.14310 (2020) - [i21]Quentin Klopfenstein, Quentin Bertrand, Alexandre Gramfort, Joseph Salmon, Samuel Vaiter:
Model identification and local linear convergence of coordinate descent. CoRR abs/2010.11825 (2020)
2010 – 2019
- 2019
- [c24]Nidham Gazagnadou, Robert M. Gower, Joseph Salmon:
Optimal Mini-Batch and Step Sizes for SAGA. ICML 2019: 2142-2150 - [c23]Eugène Ndiaye, Tam Le, Olivier Fercoq, Joseph Salmon, Ichiro Takeuchi:
Safe Grid Search with Optimal Complexity. ICML 2019: 4771-4780 - [c22]Alain Rakotomamonjy, Gilles Gasso, Joseph Salmon:
Screening rules for Lasso with non-convex Sparse Regularizers. ICML 2019: 5341-5350 - [c21]Quentin Bertrand, Mathurin Massias, Alexandre Gramfort, Joseph Salmon:
Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso. NeurIPS 2019: 3961-3972 - [c20]Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter:
Refitting Solutions Promoted by ℓ _12 Sparse Analysis Regularizations with Block Penalties. SSVM 2019: 131-143 - [i20]Nidham Gazagnadou, Robert M. Gower, Joseph Salmon:
Optimal mini-batch and step sizes for SAGA. CoRR abs/1902.00071 (2019) - [i19]Quentin Bertrand, Mathurin Massias, Alexandre Gramfort, Joseph Salmon:
Concomitant Lasso with Repetitions (CLaR): beyond averaging multiple realizations of heteroscedastic noise. CoRR abs/1902.02509 (2019) - [i18]Alain Rakotomamonjy, Gilles Gasso, Joseph Salmon:
Screening Rules for Lasso with Non-Convex Sparse Regularizers. CoRR abs/1902.06125 (2019) - [i17]Mathurin Massias, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon:
Dual Extrapolation for Sparse Generalized Linear Models. CoRR abs/1907.05830 (2019) - 2018
- [c19]Mathurin Massias, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon:
Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression. AISTATS 2018: 998-1007 - [c18]Mathurin Massias, Joseph Salmon, Alexandre Gramfort:
Celer: a Fast Solver for the Lasso with Dual Extrapolation. ICML 2018: 3321-3330 - [c17]Jérôme-Alexis Chevalier, Joseph Salmon, Bertrand Thirion:
Statistical Inference with Ensemble of Clustered Desparsified Lasso. MICCAI (1) 2018: 638-646 - [i16]Eugène Ndiaye, Tam Le, Olivier Fercoq, Joseph Salmon, Ichiro Takeuchi:
Safe Grid Search with Optimal Complexity. CoRR abs/1810.05471 (2018) - 2017
- [j8]Eugène Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon:
Gap Safe Screening Rules for Sparsity Enforcing Penalties. J. Mach. Learn. Res. 18: 128:1-128:33 (2017) - [j7]Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter:
CLEAR: Covariant LEAst-Square Refitting with Applications to Image Restoration. SIAM J. Imaging Sci. 10(1): 243-284 (2017) - [i15]Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Joseph Salmon:
On the benefits of output sparsity for multi-label classification. CoRR abs/1703.04697 (2017) - [i14]Mathurin Massias, Alexandre Gramfort, Joseph Salmon:
From safe screening rules to working sets for faster Lasso-type solvers. CoRR abs/1703.07285 (2017) - 2016
- [c16]Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon:
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions. ICML 2016: 1388-1396 - [c15]Eugène Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon:
GAP Safe Screening Rules for Sparse-Group Lasso. NIPS 2016: 388-396 - [i13]Eugène Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon:
GAP Safe Screening Rules for Sparse-Group-Lasso. CoRR abs/1602.06225 (2016) - [i12]Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon:
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions. CoRR abs/1606.02421 (2016) - [i11]Eugène Ndiaye, Olivier Fercoq, Alexandre Gramfort, Vincent Leclère, Joseph Salmon:
Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression. CoRR abs/1606.02702 (2016) - [i10]Claire Boyer, Yohann de Castro, Joseph Salmon:
Adapting to unknown noise level in sparse deconvolution. CoRR abs/1606.04760 (2016) - [i9]Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter:
CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration. CoRR abs/1606.05158 (2016) - [i8]Eugène Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon:
Gap Safe screening rules for sparsity enforcing penalties. CoRR abs/1611.05780 (2016) - 2015
- [c14]Olivier Fercoq, Alexandre Gramfort, Joseph Salmon:
Mind the duality gap: safer rules for the Lasso. ICML 2015: 333-342 - [c13]Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon:
Extending Gossip Algorithms to Distributed Estimation of U-statistics. NIPS 2015: 271-279 - [c12]Eugène Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon:
GAP Safe screening rules for sparse multi-task and multi-class models. NIPS 2015: 811-819 - [c11]Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon:
On Debiasing Restoration Algorithms: Applications to Total-Variation and Nonlocal-Means. SSVM 2015: 129-141 - [i7]Olivier Fercoq, Alexandre Gramfort, Joseph Salmon:
Mind the duality gap: safer rules for the Lasso. CoRR abs/1505.03410 (2015) - [i6]Eugène Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon:
GAP Safe screening rules for sparse multi-task and multi-class models. CoRR abs/1506.03736 (2015) - [i5]Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon:
Extending Gossip Algorithms to Distributed Estimation of U-Statistics. CoRR abs/1511.05464 (2015) - 2014
- [j6]David Günther, Joseph Salmon, Julien Tierny:
Mandatory Critical Points of 2D Uncertain Scalar Fields. Comput. Graph. Forum 33(3): 31-40 (2014) - [j5]Joseph Salmon, Zachary T. Harmany, Charles-Alban Deledalle, Rebecca Willett:
Poisson Noise Reduction with Non-local PCA. J. Math. Imaging Vis. 48(2): 279-294 (2014) - [c10]Jean Lafond, Olga Klopp, Eric Moulines, Joseph Salmon:
Probabilistic low-rank matrix completion on finite alphabets. NIPS 2014: 1727-1735 - 2013
- [c9]Arnak S. Dalalyan, Mohamed Hebiri, Katia Meziani, Joseph Salmon:
Learning Heteroscedastic Models by Convex Programming under Group Sparsity. ICML (3) 2013: 379-387 - [i4]Mohamed-Jalal Fadili, Gabriel Peyré, Samuel Vaiter, Charles-Alban Deledalle, Joseph Salmon:
Stable Recovery with Analysis Decomposable Priors. CoRR abs/1304.4407 (2013) - 2012
- [j4]Charles-Alban Deledalle, Vincent Duval, Joseph Salmon:
Non-local Methods with Shape-Adaptive Patches (NLM-SAP). J. Math. Imaging Vis. 43(2): 103-120 (2012) - [j3]Ery Arias-Castro, Joseph Salmon, Rebecca Willett:
Oracle Inequalities and Minimax Rates for Nonlocal Means and Related Adaptive Kernel-Based Methods. SIAM J. Imaging Sci. 5(3): 944-992 (2012) - [j2]Joseph Salmon, Yann Strozecki:
Patch reprojections for Non-Local methods. Signal Process. 92(2): 477-489 (2012) - [c8]Joseph Salmon, Charles-Alban Deledalle, Rebecca Willett, Zachary T. Harmany:
Poisson noise reduction with non-local PCA. ICASSP 2012: 1109-1112 - [c7]Joseph Salmon, Rebecca Willett, Ery Arias-Castro:
A two-stage denoising filter: The preprocessed Yaroslavsky filter. SSP 2012: 464-467 - [i3]Joseph Salmon, Zachary T. Harmany, Charles-Alban Deledalle, Rebecca Willett:
Poisson noise reduction with non-local PCA. CoRR abs/1206.0338 (2012) - [i2]Joseph Salmon, Rebecca Willett, Ery Arias-Castro:
A two-stage denoising filter: the preprocessed Yaroslavsky filter. CoRR abs/1208.6516 (2012) - 2011
- [c6]Arnak S. Dalalyan, Joseph Salmon:
Competing against the Best Nearest Neighbor Filter in Regression. ALT 2011: 129-143 - [c5]Charles-Alban Deledalle, Joseph Salmon, Arnak S. Dalalyan:
Image denoising with patch based PCA: local versus global. BMVC 2011: 1-10 - [c4]Charles-Alban Deledalle, Vincent Duval, Joseph Salmon:
Anisotropic Non-Local Means with Spatially Adaptive Patch Shapes. SSVM 2011: 231-242 - [c3]Joseph Salmon, Arnak S. Dalalyan:
Optimal aggregation of affine estimators. COLT 2011: 635-660 - [i1]Ery Arias-Castro, Joseph Salmon, Rebecca Willett:
Oracle inequalities and minimax rates for non-local means and related adaptive kernel-based methods. CoRR abs/1112.4434 (2011) - 2010
- [j1]Joseph Salmon:
On Two Parameters for Denoising With Non-Local Means. IEEE Signal Process. Lett. 17(3): 269-272 (2010) - [c2]Joseph Salmon, Yann Strozecki:
From patches to pixels in Non-Local methods: Weighted-average reprojection. ICIP 2010: 1929-1932
2000 – 2009
- 2009
- [c1]Joseph Salmon, Erwan Le Pennec:
NL-Means and aggregation procedures. ICIP 2009: 2977-2980
Coauthor Index
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