Samuel Vaiter

ORCID

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 47

Top Venues

CoRR

NeurIPS

J. Math. Imaging Vis.

SIAM J. Optim.

Paris Dauphine University, France (PHD)

Education History

Paris Dauphine University, France

PHD

Publications

Franck Iutzeler, Edouard Pauwels, Samuel Vaiter
Derivatives of Stochastic Gradient Descent. CoRR (2024)
Jérôme Bolte, Quoc-Tung Le, Edouard Pauwels, Samuel Vaiter
Geometric and computational hardness of bilevel programming. CoRR (2024)
Sophie Jaffard, Samuel Vaiter, Alexandre Muzy, Patricia Reynaud-Bouret
Provable local learning rule by expert aggregation for a Hawkes network. AISTATS (2024)
Mathieu Dagréou, Thomas Moreau, Samuel Vaiter, Pierre Ablin
A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization. AISTATS (2024)
Hashem Ghanem, Samuel Vaiter, Nicolas Keriven
Gradient Scarcity in Graph Learning with Bilevel Optimization. Trans. Mach. Learn. Res. 2024 (2024)
Quentin Klopfenstein, Quentin Bertrand, Alexandre Gramfort, Joseph Salmon, Samuel Vaiter
Local linear convergence of proximal coordinate descent algorithm. Optim. Lett. 18 (1) (2024)
Xavier Dupuis, Samuel Vaiter
The Geometry of Sparse Analysis Regularization. SIAM J. Optim. 33 (2) (2023)
Jérôme Bolte, Edouard Pauwels, Samuel Vaiter
One-step differentiation of iterative algorithms. NeurIPS (2023)
Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter
Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs. CoRR (2023)
Hashem Ghanem, Joseph Salmon, Nicolas Keriven, Samuel Vaiter
Supervised Learning of Analysis-Sparsity Priors With Automatic Differentiation. IEEE Signal Process. Lett. 30 (2023)
Nicolas Keriven, Samuel Vaiter
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding. CoRR (2023)
Mathieu Dagréou, Thomas Moreau, Samuel Vaiter, Pierre Ablin
A Near-Optimal Algorithm for Bilevel Empirical Risk Minimization. CoRR (2023)
Rémi Catellier, Samuel Vaiter, Damien Garreau
On the Robustness of Text Vectorizers. CoRR (2023)
Edouard Pauwels, Samuel Vaiter
The Derivatives of Sinkhorn-Knopp Converge. SIAM J. Optim. 33 (3) (2023)
Hashem Ghanem, Samuel Vaiter, Nicolas Keriven
Gradient scarcity with Bilevel Optimization for Graph Learning. CoRR (2023)
Rémi Catellier, Samuel Vaiter, Damien Garreau
On the Robustness of Text Vectorizers. ICML (2023)
Jérôme Bolte, Edouard Pauwels, Samuel Vaiter
One-step differentiation of iterative algorithms. CoRR (2023)
Nicolas Keriven, Samuel Vaiter
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding. NeurIPS (2023)
Jérôme Bolte, Edouard Pauwels, Samuel Vaiter
Automatic differentiation of nonsmooth iterative algorithms. NeurIPS (2022)
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 (2022)
Mathieu Dagréou, Pierre Ablin, Samuel Vaiter, Thomas Moreau
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms. CoRR (2022)
Jérôme Bolte, Edouard Pauwels, Samuel Vaiter
Automatic differentiation of nonsmooth iterative algorithms. CoRR (2022)
Mathieu Dagréou, Pierre Ablin, Samuel Vaiter, Thomas Moreau
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms. NeurIPS (2022)
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 (2022)
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)
Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter
Sparse Regularization. J. Math. Imaging Vis. 63 (2) (2021)
Barbara Pascal, Samuel Vaiter, Nelly Pustelnik, Patrice Abry
Automated Data-Driven Selection of the Hyperparameters for Total-Variation-Based Texture Segmentation. J. Math. Imaging Vis. 63 (7) (2021)
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 (2021)
Quentin Klopfenstein, Samuel Vaiter
Linear support vector regression with linear constraints. Mach. Learn. 110 (7) (2021)
Nicolas Keriven, Alberto Bietti, Samuel Vaiter
On the Universality of Graph Neural Networks on Large Random Graphs. NeurIPS (2021)
Nicolas Keriven, Alberto Bietti, Samuel Vaiter
On the Universality of Graph Neural Networks on Large Random Graphs. CoRR (2021)
Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon
Implicit differentiation of Lasso-type models for hyperparameter optimization. CoRR (2020)
Nicolas Keriven, Alberto Bietti, Samuel Vaiter
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. NeurIPS (2020)
Barbara Pascal, Samuel Vaiter, Nelly Pustelnik, Patrice Abry
Automated data-driven selection of the hyperparameters for Total-Variation based texture segmentation. CoRR (2020)
Nicolas Keriven, Alberto Bietti, Samuel Vaiter
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. CoRR (2020)
Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon
Implicit differentiation of Lasso-type models for hyperparameter optimization. ICML (2020)
Nicolas Keriven, Samuel Vaiter
Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model. CoRR (2020)
Quentin Klopfenstein, Quentin Bertrand, Alexandre Gramfort, Joseph Salmon, Samuel Vaiter
Model identification and local linear convergence of coordinate descent. CoRR (2020)
Abdessamad Barbara, Abderrahim Jourani, Samuel Vaiter
Maximal Solutions of Sparse Analysis Regularization. J. Optim. Theory Appl. 180 (2) (2019)
Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter
Refitting Solutions Promoted by ℓ _12 Sparse Analysis Regularizations with Block Penalties. SSVM (2019)
Mathurin Massias, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon
Dual Extrapolation for Sparse Generalized Linear Models. CoRR (2019)
Yann Traonmilin, Samuel Vaiter, Rémi Gribonval
Is the 1-norm the best convex sparse regularization? CoRR (2018)
Samuel Vaiter, Gabriel Peyré, Jalal Fadili
Model Consistency of Partly Smooth Regularizers. IEEE Trans. Inf. Theory 64 (3) (2018)
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 (2018)
Antonin Chambolle, Pauline Tan, Samuel Vaiter
Accelerated Alternating Descent Methods for Dykstra-Like Problems. J. Math. Imaging Vis. 59 (3) (2017)
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) (2017)
Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter
CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration. CoRR (2016)
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 (2014)
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) (2014)
Samuel Vaiter, Charles-Alban Deledalle, Gabriel Peyré, Jalal Fadili, Charles Dossal
The Degrees of Freedom of Partly Smooth Regularizers. CoRR (2014)
Samuel Vaiter, Gabriel Peyré, Jalal Fadili
Low Complexity Regularization of Linear Inverse Problems. CoRR (2014)
Samuel Vaiter, Gabriel Peyré, Jalal Fadili
Partly Smooth Regularization of Inverse Problems. CoRR (2014)
Samuel Vaiter, Mohammad Golbabaee, Jalal Fadili, Gabriel Peyré
Model Selection with Piecewise Regular Gauges. CoRR (2013)
Samuel Vaiter, Gabriel Peyré, Charles Dossal, Jalal Fadili
Robust Sparse Analysis Regularization. IEEE Trans. Inf. Theory 59 (4) (2013)
Mohamed-Jalal Fadili, Gabriel Peyré, Samuel Vaiter, Charles-Alban Deledalle, Joseph Salmon
Stable Recovery with Analysis Decomposable Priors CoRR (2013)
Samuel Vaiter, Gabriel Peyré, Jalal Fadili
Robust Polyhedral Regularization CoRR (2013)
Samuel Vaiter, Charles-Alban Deledalle, Gabriel Peyré, Jalal Fadili, Charles Dossal
The degrees of freedom of the Group Lasso for a General Design CoRR (2012)
Charles-Alban Deledalle, Samuel Vaiter, Gabriel Peyré, Jalal Fadili, Charles Dossal
Unbiased risk estimation for sparse analysis regularization. ICIP (2012)
Charles-Alban Deledalle, Samuel Vaiter, Gabriel Peyré, Jalal Fadili, Charles Dossal
Risk estimation for matrix recovery with spectral regularization CoRR (2012)
Samuel Vaiter, Gabriel Peyré, Charles Dossal, Jalal Fadili
Robust Sparse Analysis Regularization CoRR (2011)

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Publication Activity (10 Years)

Top Topics

Top Venues

Samuel Vaiter