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Samuel Vaiter
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- affiliation: Université Côte d'Azur, Nice, France
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2020 – today
- 2024
- [j15]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) - [j14]Hashem Ghanem, Samuel Vaiter, Nicolas Keriven:
Gradient Scarcity in Graph Learning with Bilevel Optimization. Trans. Mach. Learn. Res. 2024 (2024) - [c13]Mathieu Dagréou, Thomas Moreau, Samuel Vaiter, Pierre Ablin:
A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization. AISTATS 2024: 82-90 - [c12]Sophie Jaffard, Samuel Vaiter, Alexandre Muzy, Patricia Reynaud-Bouret:
Provable local learning rule by expert aggregation for a Hawkes network. AISTATS 2024: 1837-1845 - [i32]Franck Iutzeler, Edouard Pauwels, Samuel Vaiter:
Derivatives of Stochastic Gradient Descent. CoRR abs/2405.15894 (2024) - [i31]Jérôme Bolte, Quoc-Tung Le, Edouard Pauwels, Samuel Vaiter:
Geometric and computational hardness of bilevel programming. CoRR abs/2407.12372 (2024) - 2023
- [j13]Xavier Dupuis, Samuel Vaiter
The Geometry of Sparse Analysis Regularization. SIAM J. Optim. 33(2): 842-867 (2023) - [j12]Edouard Pauwels
The Derivatives of Sinkhorn-Knopp Converge. SIAM J. Optim. 33(3): 1494-1517 (2023) - [j11]Hashem Ghanem
Supervised Learning of Analysis-Sparsity Priors With Automatic Differentiation. IEEE Signal Process. Lett. 30: 339-343 (2023) - [c11]Rémi Catellier, Samuel Vaiter, Damien Garreau:
On the Robustness of Text Vectorizers. ICML 2023: 3782-3814 - [c10]Jérôme Bolte, Edouard Pauwels, Samuel Vaiter:
One-step differentiation of iterative algorithms. NeurIPS 2023 - [c9]Nicolas Keriven, Samuel Vaiter:
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding. NeurIPS 2023 - [i30]Mathieu Dagréou
A Near-Optimal Algorithm for Bilevel Empirical Risk Minimization. CoRR abs/2302.08766 (2023) - [i29]Rémi Catellier, Samuel Vaiter, Damien Garreau:
On the Robustness of Text Vectorizers. CoRR abs/2303.07203 (2023) - [i28]Hashem Ghanem, Samuel Vaiter, Nicolas Keriven:
Gradient scarcity with Bilevel Optimization for Graph Learning. CoRR abs/2303.13964 (2023) - [i27]Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter:
Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs. CoRR abs/2304.11140 (2023) - [i26]Jérôme Bolte, Edouard Pauwels, Samuel Vaiter:
One-step differentiation of iterative algorithms. CoRR abs/2305.13768 (2023) - [i25]Nicolas Keriven, Samuel Vaiter:
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding. CoRR abs/2305.14814 (2023) - 2022
- [j10]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) - [c8]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 - [c7]Jérôme Bolte, Edouard Pauwels, Samuel Vaiter:
Automatic differentiation of nonsmooth iterative algorithms. NeurIPS 2022 - [c6]Mathieu Dagréou, Pierre Ablin, Samuel Vaiter, Thomas Moreau:
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms. NeurIPS 2022 - [i24]Mathieu Dagréou, Pierre Ablin, Samuel Vaiter, Thomas Moreau:
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms. CoRR abs/2201.13409 (2022) - [i23]Jérôme Bolte, Edouard Pauwels, Samuel Vaiter:
Automatic differentiation of nonsmooth iterative algorithms. CoRR abs/2206.00457 (2022) - [i22]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
Benchopt: Reproducible, efficient and collaborative optimization benchmarks. CoRR abs/2206.13424 (2022) - 2021
- [j9]Charles-Alban Deledalle, Nicolas Papadakis
Block-Based Refitting in ℓ 12 Sparse Regularization. J. Math. Imaging Vis. 63(2): 216-236 (2021) - [j8]Barbara Pascal
Automated Data-Driven Selection of the Hyperparameters for Total-Variation-Based Texture Segmentation. J. Math. Imaging Vis. 63(7): 923-952 (2021) - [j7]Quentin Klopfenstein, Samuel Vaiter
Linear support vector regression with linear constraints. Mach. Learn. 110(7): 1939-1974 (2021) - [c5]Nicolas Keriven, Alberto Bietti, Samuel Vaiter:
On the Universality of Graph Neural Networks on Large Random Graphs. NeurIPS 2021: 6960-6971 - [i21]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) - [i20]Nicolas Keriven, Alberto Bietti, Samuel Vaiter:
On the Universality of Graph Neural Networks on Large Random Graphs. CoRR abs/2105.13099 (2021) - 2020
- [c4]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 - [c3]Nicolas Keriven, Alberto Bietti, Samuel Vaiter:
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. NeurIPS 2020 - [i19]Nicolas Keriven
Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model. CoRR abs/2002.02892 (2020) - [i18]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) - [i17]Barbara Pascal, Samuel Vaiter, Nelly Pustelnik, Patrice Abry:
Automated data-driven selection of the hyperparameters for Total-Variation based texture segmentation. CoRR abs/2004.09434 (2020) - [i16]Nicolas Keriven
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. CoRR abs/2006.01868 (2020) - [i15]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
- [j6]Abdessamad Barbara, Abderrahim Jourani
Maximal Solutions of Sparse Analysis Regularization. J. Optim. Theory Appl. 180(2): 374-396 (2019) - [c2]Charles-Alban Deledalle
Refitting Solutions Promoted by ℓ _12 Sparse Analysis Regularizations with Block Penalties. SSVM 2019: 131-143 - [i14]Mathurin Massias, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon:
Dual Extrapolation for Sparse Generalized Linear Models. CoRR abs/1907.05830 (2019) - 2018
- [j5]Samuel Vaiter, Gabriel Peyré, Jalal Fadili:
Model Consistency of Partly Smooth Regularizers. IEEE Trans. Inf. Theory 64(3): 1725-1737 (2018) - [i13]Yann Traonmilin, Samuel Vaiter:
Optimality of 1-norm regularization among weighted 1-norms for sparse recovery: a case study on how to find optimal regularizations. CoRR abs/1803.00773 (2018) - [i12]Yann Traonmilin, Samuel Vaiter, Rémi Gribonval:
Is the 1-norm the best convex sparse regularization? CoRR abs/1806.08690 (2018) - 2017
- [j4]Antonin Chambolle
Accelerated Alternating Descent Methods for Dykstra-Like Problems. J. Math. Imaging Vis. 59(3): 481-497 (2017) - [j3]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) - 2016
- [i11]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) - 2014
- [b1]Samuel Vaiter:
Low Complexity Regularizations of Inverse Problems. (Régularisations de Faible Complexité pour les Problèmes Inverses). Paris Dauphine University, France, 2014 - [j2]Charles-Alban Deledalle, Samuel Vaiter, Jalal Fadili, Gabriel Peyré
Stein Unbiased GrAdient estimator of the Risk (SUGAR) for Multiple Parameter Selection. SIAM J. Imaging Sci. 7(4): 2448-2487 (2014) - [i10]Samuel Vaiter, Charles-Alban Deledalle, Gabriel Peyré, Jalal Fadili, Charles Dossal:
The Degrees of Freedom of Partly Smooth Regularizers. CoRR abs/1404.5557 (2014) - [i9]Samuel Vaiter, Gabriel Peyré, Jalal Fadili:
Partly Smooth Regularization of Inverse Problems. CoRR abs/1405.1004 (2014) - [i8]Samuel Vaiter, Gabriel Peyré, Jalal Fadili:
Low Complexity Regularization of Linear Inverse Problems. CoRR abs/1407.1598 (2014) - [i7]Laurent Jacques, Christophe De Vleeschouwer, Yannick Boursier, Prasad Sudhakar, C. De Mol, Aleksandra Pizurica, Sandrine Anthoine, Pierre Vandergheynst, Pascal Frossard, Cagdas Bilen, Srdan Kitic, Nancy Bertin, Rémi Gribonval, Nicolas Boumal, Bamdev Mishra, Pierre-Antoine Absil, Rodolphe Sepulchre, Shaun Bundervoet, Colas Schretter, Ann Dooms, Peter Schelkens, Olivier Chabiron, François Malgouyres, Jean-Yves Tourneret, Nicolas Dobigeon, Pierre Chainais, Cédric Richard, Bruno Cornelis, Ingrid Daubechies, David B. Dunson, Marie Danková, Pavel Rajmic, Kévin Degraux, Valerio Cambareri, Bert Geelen, Gauthier Lafruit, Gianluca Setti, Jean-François Determe, Jérôme Louveaux, François Horlin, Angélique Drémeau, Patrick Héas, Cédric Herzet, Vincent Duval, Gabriel Peyré, Alhussein Fawzi, Mike E. Davies, Nicolas Gillis, Stephen A. Vavasis, Charles Soussen, Luc Le Magoarou, Jingwei Liang, Jalal Fadili, Antoine Liutkus, David Martina, Sylvain Gigan, Laurent Daudet, Mauro Maggioni, Stanislav Minsker, Nate Strawn, C. Mory, Fred Maurice Ngolè Mboula, Jean-Luc Starck, Ignace Loris, Samuel Vaiter, Mohammad Golbabaee, Dejan Vukobratovic:
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14). CoRR abs/1410.0719 (2014) - 2013
- [j1]Samuel Vaiter, Gabriel Peyré
Robust Sparse Analysis Regularization. IEEE Trans. Inf. Theory 59(4): 2001-2016 (2013) - [i6]Mohamed-Jalal Fadili, Gabriel Peyré, Samuel Vaiter, Charles-Alban Deledalle, Joseph Salmon:
Stable Recovery with Analysis Decomposable Priors. CoRR abs/1304.4407 (2013) - [i5]Samuel Vaiter, Gabriel Peyré, Jalal Fadili:
Robust Polyhedral Regularization. CoRR abs/1304.6033 (2013) - [i4]Samuel Vaiter, Mohammad Golbabaee, Jalal Fadili, Gabriel Peyré:
Model Selection with Piecewise Regular Gauges. CoRR abs/1307.2342 (2013) - 2012
- [c1]Charles-Alban Deledalle, Samuel Vaiter, Gabriel Peyré, Jalal Fadili, Charles Dossal:
Unbiased risk estimation for sparse analysis regularization. ICIP 2012: 3053-3056 - [i3]Charles-Alban Deledalle, Samuel Vaiter, Gabriel Peyré, Jalal Fadili, Charles Dossal:
Risk estimation for matrix recovery with spectral regularization. CoRR abs/1205.1482 (2012) - [i2]Samuel Vaiter, Charles-Alban Deledalle, Gabriel Peyré, Jalal Fadili, Charles Dossal:
The degrees of freedom of the Group Lasso for a General Design. CoRR abs/1212.6478 (2012) - 2011
- [i1]Samuel Vaiter, Gabriel Peyré, Charles Dossal, Jalal Fadili:
Robust Sparse Analysis Regularization. CoRR abs/1109.6222 (2011)
Coauthor Index
aka: Jalal Fadili
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