Unbiased risk estimation for sparse analysis regularization. - Charles-Alban Deledalle; Samuel Vaiter; Gabriel Peyré; Jalal Fadili; Charles Dossal (ICIP, 2012)

Risk estimation for matrix recovery with spectral regularization - Charles-Alban Deledalle; Samuel Vaiter; Gabriel Peyré; Jalal Fadili; Charles Dossal (CoRR, 2012)

The degrees of freedom of the Group Lasso for a General Design - Samuel Vaiter; Charles-Alban Deledalle; Gabriel Peyré; Jalal Fadili; Charles Dossal (CoRR, 2012)

Model Selection with Piecewise Regular Gauges. - Samuel Vaiter; Mohammad Golbabaee; Jalal Fadili; Gabriel Peyré (CoRR, 2013)

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

Robust Polyhedral Regularization - Samuel Vaiter; Gabriel Peyré; Jalal Fadili (CoRR, 2013)

Robust Sparse Analysis Regularization. - Samuel Vaiter; Gabriel Peyré; Charles Dossal; Jalal Fadili (IEEE Trans. Inf. Theory, 2013)

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14). - 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; et al. (CoRR, 2014)

The Degrees of Freedom of Partly Smooth Regularizers. - Samuel Vaiter; Charles-Alban Deledalle; Gabriel Peyré; Jalal Fadili; Charles Dossal (CoRR, 2014)

Partly Smooth Regularization of Inverse Problems. - Samuel Vaiter; Gabriel Peyré; Jalal Fadili (CoRR, 2014)

Low Complexity Regularization of Linear Inverse Problems. - Samuel Vaiter; Gabriel Peyré; Jalal Fadili (CoRR, 2014)

Stein Unbiased GrAdient estimator of the Risk (SUGAR) for Multiple Parameter Selection. - Charles-Alban Deledalle; Samuel Vaiter; Jalal Fadili; Gabriel Peyré (SIAM J. Imaging Sci., 2014)

Low Complexity Regularizations of Inverse Problems. (Régularisations de Faible Complexité pour les Problèmes Inverses). - Samuel Vaiter (2014)

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

Accelerated Alternating Descent Methods for Dykstra-Like Problems. - Antonin Chambolle; Pauline Tan; Samuel Vaiter (J. Math. Imaging Vis., 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)

Optimality of 1-norm regularization among weighted 1-norms for sparse recovery: a case study on how to find optimal regularizations. - Yann Traonmilin; Samuel Vaiter (CoRR, 2018)

Is the 1-norm the best convex sparse regularization? - Yann Traonmilin; Samuel Vaiter; Rémi Gribonval (CoRR, 2018)

Model Consistency of Partly Smooth Regularizers. - Samuel Vaiter; Gabriel Peyré; Jalal Fadili (IEEE Trans. Inf. Theory, 2018)

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

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

Maximal Solutions of Sparse Analysis Regularization. - Abdessamad Barbara; Abderrahim Jourani; Samuel Vaiter (J. Optim. Theory Appl., 2019)

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

Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. - Nicolas Keriven; Alberto Bietti; Samuel Vaiter (NeurIPS, 2020)

Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model. - Nicolas Keriven; Samuel Vaiter (CoRR, 2020)

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

On the Universality of Graph Neural Networks on Large Random Graphs. - Nicolas Keriven; Alberto Bietti; Samuel Vaiter (NeurIPS, 2021)

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

Automated Data-Driven Selection of the Hyperparameters for Total-Variation-Based Texture Segmentation. - Barbara Pascal; Samuel Vaiter; Nelly Pustelnik; Patrice Abry (J. Math. Imaging Vis., 2021)

Linear support vector regression with linear constraints. - Quentin Klopfenstein; Samuel Vaiter (Mach. Learn., 2021)

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)

Automatic differentiation of nonsmooth iterative algorithms. - Jérôme Bolte; Edouard Pauwels; Samuel Vaiter (NeurIPS, 2022)

A framework for bilevel optimization that enables stochastic and global variance reduction algorithms. - Mathieu Dagréou; Pierre Ablin; Samuel Vaiter; Thomas Moreau 001 (NeurIPS, 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)

On the Robustness of Text Vectorizers. - Rémi Catellier; Samuel Vaiter; Damien Garreau (ICML, 2023)

One-step differentiation of iterative algorithms. - Jérôme Bolte; Edouard Pauwels; Samuel Vaiter (NeurIPS, 2023)

What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding. - Nicolas Keriven; Samuel Vaiter (NeurIPS, 2023)

A Near-Optimal Algorithm for Bilevel Empirical Risk Minimization. - Mathieu Dagréou; Thomas Moreau 001; Samuel Vaiter; Pierre Ablin (CoRR, 2023)

Gradient scarcity with Bilevel Optimization for Graph Learning. - Hashem Ghanem; Samuel Vaiter; Nicolas Keriven (CoRR, 2023)

Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs. - Matthieu Cordonnier; Nicolas Keriven; Nicolas Tremblay; Samuel Vaiter (CoRR, 2023)

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

The Geometry of Sparse Analysis Regularization. - Xavier Dupuis; Samuel Vaiter (SIAM J. Optim., 2023)

The Derivatives of Sinkhorn-Knopp Converge. - Edouard Pauwels; Samuel Vaiter (SIAM J. Optim., 2023)

Seeking Universal Approximation for Continuous Limits of Graph Neural Networks on Large Random Graphs. - Matthieu Cordonnier; Nicolas Keriven; Nicolas Tremblay; Samuel Vaiter (IEEECONF, 2024)

A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization. - Mathieu Dagréou; Thomas Moreau 001; Samuel Vaiter; Pierre Ablin (AISTATS, 2024)

Provable local learning rule by expert aggregation for a Hawkes network. - Sophie Jaffard; Samuel Vaiter; Alexandre Muzy; Patricia Reynaud-Bouret (AISTATS, 2024)

Derivatives of Stochastic Gradient Descent in parametric optimization. - Franck Iutzeler; Edouard Pauwels; Samuel Vaiter (NeurIPS, 2024)

Derivatives of Stochastic Gradient Descent. - Franck Iutzeler; Edouard Pauwels; Samuel Vaiter (CoRR, 2024)

Geometric and computational hardness of bilevel programming. - Jérôme Bolte; Quoc-Tung Le; Edouard Pauwels; Samuel Vaiter (CoRR, 2024)

Gradient Scarcity in Graph Learning with Bilevel Optimization. - Hashem Ghanem; Samuel Vaiter; Nicolas Keriven (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)

Learning Theory for Kernel Bilevel Optimization. - Fares El Khoury; Edouard Pauwels; Samuel Vaiter; Michael Arbel (CoRR, 2025)

Differentiable Generalized Sliced Wasserstein Plans. - Laetitia Chapel; Romain Tavenard; Samuel Vaiter (CoRR, 2025)

Faster Computation of Entropic Optimal Transport via Stable Low Frequency Modes. - Reda Chhaibi; Serge Gratton; Samuel Vaiter (CoRR, 2025)

Deep Equilibrium models for Poisson Imaging Inverse problems via Mirror Descent. - Christian Daniele; Silvia Villa; Samuel Vaiter; Luca Calatroni (CoRR, 2025)