Samuel Vaiter - RankMe

74 papers A* 10A 2B 1Journal 58Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	CHANI: Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration. J. Mach. Learn. Res. Sophie Jaffard, Samuel Vaiter, Patricia Reynaud-Bouret
2026	J	`jnl`	Fairness-informed Pareto Optimization : An Efficient Bilevel Framework. CoRR Sofiane Tanji, Samuel Vaiter, Yassine Laguel
2026	J	`jnl`	Geometric and computational hardness of bilevel programming. Math. Program. Jérôme Bolte, Quoc-Tung Le, Edouard Pauwels, Samuel Vaiter
2026	J	`jnl`	Risk estimate under a time-varying autoregressive model for data-driven reproduction number estimation. Signal Process. Barbara Pascal, Samuel Vaiter
2026	J	`jnl`	Towards Understanding Steering Strength. CoRR Magamed Taimeskhanov, Samuel Vaiter, Damien Garreau
2025	J	`jnl`	Deep Equilibrium models for Poisson Imaging Inverse problems via Mirror Descent. CoRR Christian Daniele, Silvia Villa, Samuel Vaiter, Luca Calatroni
2025	J	`jnl`	Differentiable Generalized Sliced Wasserstein Plans. CoRR Laetitia Chapel, Romain Tavenard, Samuel Vaiter
2025	J	`jnl`	Faster Computation of Entropic Optimal Transport via Stable Low Frequency Modes. CoRR Reda Chhaibi, Serge Gratton, Samuel Vaiter
2025	J	`jnl`	From Shortcut to Induction Head: How Data Diversity Shapes Algorithm Selection in Transformers. CoRR Ryotaro Kawata, Yujin Song, Alberto Bietti, Naoki Nishikawa, Taiji Suzuki, Samuel Vaiter, Denny Wu
2025	J	`jnl`	Learning Theory for Kernel Bilevel Optimization. CoRR Fares El Khoury, Edouard Pauwels, Samuel Vaiter, Michael Arbel
2024	A	`conf`	A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization. AISTATS Mathieu Dagréou, Thomas Moreau, Samuel Vaiter, Pierre Ablin
2024	J	`jnl`	Convergence of Message-Passing Graph Neural Networks with Generic Aggregation on Large Random Graphs. J. Mach. Learn. Res. Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter
2024	A*	`conf`	Derivatives of Stochastic Gradient Descent in parametric optimization. NeurIPS Franck Iutzeler, Edouard Pauwels, Samuel Vaiter
2024	J	`jnl`	Derivatives of Stochastic Gradient Descent. CoRR Franck Iutzeler, Edouard Pauwels, Samuel Vaiter
2024	J	`jnl`	Geometric and computational hardness of bilevel programming. CoRR Jérôme Bolte, Quoc-Tung Le, Edouard Pauwels, Samuel Vaiter
2024	J	`jnl`	Gradient Scarcity in Graph Learning with Bilevel Optimization. Trans. Mach. Learn. Res. Hashem Ghanem, Samuel Vaiter, Nicolas Keriven
2024	J	`jnl`	Local linear convergence of proximal coordinate descent algorithm. Optim. Lett. Quentin Klopfenstein, Quentin Bertrand, Alexandre Gramfort, Joseph Salmon, Samuel Vaiter
2024	A	`conf`	Provable local learning rule by expert aggregation for a Hawkes network. AISTATS Sophie Jaffard, Samuel Vaiter, Alexandre Muzy, Patricia Reynaud-Bouret
2024	—	`conf`	Seeking Universal Approximation for Continuous Limits of Graph Neural Networks on Large Random Graphs. IEEECONF Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter
2023	J	`jnl`	A Near-Optimal Algorithm for Bilevel Empirical Risk Minimization. CoRR Mathieu Dagréou, Thomas Moreau, Samuel Vaiter, Pierre Ablin
2023	J	`jnl`	Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs. CoRR Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter
2023	J	`jnl`	Gradient scarcity with Bilevel Optimization for Graph Learning. CoRR Hashem Ghanem, Samuel Vaiter, Nicolas Keriven
2023	A*	`conf`	On the Robustness of Text Vectorizers. ICML Rémi Catellier, Samuel Vaiter, Damien Garreau
2023	J	`jnl`	On the Robustness of Text Vectorizers. CoRR Rémi Catellier, Samuel Vaiter, Damien Garreau
2023	A*	`conf`	One-step differentiation of iterative algorithms. NeurIPS Jérôme Bolte, Edouard Pauwels, Samuel Vaiter
2023	J	`jnl`	One-step differentiation of iterative algorithms. CoRR Jérôme Bolte, Edouard Pauwels, Samuel Vaiter
2023	J	`jnl`	Supervised Learning of Analysis-Sparsity Priors With Automatic Differentiation. IEEE Signal Process. Lett. Hashem Ghanem, Joseph Salmon, Nicolas Keriven, Samuel Vaiter
2023	J	`jnl`	The Derivatives of Sinkhorn-Knopp Converge. SIAM J. Optim. Edouard Pauwels, Samuel Vaiter
2023	J	`jnl`	The Geometry of Sparse Analysis Regularization. SIAM J. Optim. Xavier Dupuis, Samuel Vaiter
2023	A*	`conf`	What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding. NeurIPS Nicolas Keriven, Samuel Vaiter
2023	J	`jnl`	What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding. CoRR Nicolas Keriven, Samuel Vaiter
2022	A*	`conf`	A framework for bilevel optimization that enables stochastic and global variance reduction algorithms. NeurIPS Mathieu Dagréou, Pierre Ablin, Samuel Vaiter, Thomas Moreau
2022	J	`jnl`	A framework for bilevel optimization that enables stochastic and global variance reduction algorithms. CoRR Mathieu Dagréou, Pierre Ablin, Samuel Vaiter, Thomas Moreau
2022	A*	`conf`	Automatic differentiation of nonsmooth iterative algorithms. NeurIPS Jérôme Bolte, Edouard Pauwels, Samuel Vaiter
2022	J	`jnl`	Automatic differentiation of nonsmooth iterative algorithms. CoRR Jérôme Bolte, Edouard Pauwels, Samuel Vaiter
2022	A*	`conf`	Benchopt: Reproducible, efficient and collaborative optimization benchmarks. NeurIPS 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
2022	J	`jnl`	Benchopt: Reproducible, efficient and collaborative optimization benchmarks. CoRR 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
2022	J	`jnl`	Implicit Differentiation for Fast Hyperparameter Selection in Non-Smooth Convex Learning. J. Mach. Learn. Res. Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon
2021	J	`jnl`	Automated Data-Driven Selection of the Hyperparameters for Total-Variation-Based Texture Segmentation. J. Math. Imaging Vis. Barbara Pascal, Samuel Vaiter, Nelly Pustelnik, Patrice Abry
2021	J	`jnl`	Block-Based Refitting in ℓ J. Math. Imaging Vis. Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter
2021	J	`jnl`	Implicit differentiation for fast hyperparameter selection in non-smooth convex learning. CoRR Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon
2021	J	`jnl`	Linear support vector regression with linear constraints. Mach. Learn. Quentin Klopfenstein, Samuel Vaiter
2021	A*	`conf`	On the Universality of Graph Neural Networks on Large Random Graphs. NeurIPS Nicolas Keriven, Alberto Bietti, Samuel Vaiter
2021	J	`jnl`	On the Universality of Graph Neural Networks on Large Random Graphs. CoRR Nicolas Keriven, Alberto Bietti, Samuel Vaiter
2020	J	`jnl`	Automated data-driven selection of the hyperparameters for Total-Variation based texture segmentation. CoRR Barbara Pascal, Samuel Vaiter, Nelly Pustelnik, Patrice Abry
2020	A*	`conf`	Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. NeurIPS Nicolas Keriven, Alberto Bietti, Samuel Vaiter
2020	J	`jnl`	Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. CoRR Nicolas Keriven, Alberto Bietti, Samuel Vaiter
2020	A*	`conf`	Implicit differentiation of Lasso-type models for hyperparameter optimization. ICML Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon
2020	J	`jnl`	Implicit differentiation of Lasso-type models for hyperparameter optimization. CoRR Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon
2020	J	`jnl`	Model identification and local linear convergence of coordinate descent. CoRR Quentin Klopfenstein, Quentin Bertrand, Alexandre Gramfort, Joseph Salmon, Samuel Vaiter
2020	J	`jnl`	Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model. CoRR Nicolas Keriven, Samuel Vaiter
2019	J	`jnl`	Dual Extrapolation for Sparse Generalized Linear Models. CoRR Mathurin Massias, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon
2019	J	`jnl`	Maximal Solutions of Sparse Analysis Regularization. J. Optim. Theory Appl. Abdessamad Barbara, Abderrahim Jourani, Samuel Vaiter
2019	—	`conf`	Refitting Solutions Promoted by ℓ _12 Sparse Analysis Regularizations with Block Penalties. SSVM Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter
2018	J	`jnl`	Is the 1-norm the best convex sparse regularization? CoRR Yann Traonmilin, Samuel Vaiter, Rémi Gribonval
2018	J	`jnl`	Model Consistency of Partly Smooth Regularizers. IEEE Trans. Inf. Theory Samuel Vaiter, Gabriel Peyré, Jalal Fadili
2018	J	`jnl`	Optimality of 1-norm regularization among weighted 1-norms for sparse recovery: a case study on how to find optimal regularizations. CoRR Yann Traonmilin, Samuel Vaiter
2017	J	`jnl`	Accelerated Alternating Descent Methods for Dykstra-Like Problems. J. Math. Imaging Vis. Antonin Chambolle, Pauline Tan, Samuel Vaiter
2017	J	`jnl`	CLEAR: Covariant LEAst-Square Refitting with Applications to Image Restoration. SIAM J. Imaging Sci. Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter
2016	J	`jnl`	CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration. CoRR Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter
2014	J	`jnl`	Low Complexity Regularization of Linear Inverse Problems. CoRR Samuel Vaiter, Gabriel Peyré, Jalal Fadili
2014	—	`—`	Low Complexity Regularizations of Inverse Problems. (Régularisations de Faible Complexité pour les Problèmes Inverses). Samuel Vaiter
2014	J	`jnl`	Partly Smooth Regularization of Inverse Problems. CoRR Samuel Vaiter, Gabriel Peyré, Jalal Fadili
2014	J	`jnl`	Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14). CoRR 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
2014	J	`jnl`	Stein Unbiased GrAdient estimator of the Risk (SUGAR) for Multiple Parameter Selection. SIAM J. Imaging Sci. Charles-Alban Deledalle, Samuel Vaiter, Jalal Fadili, Gabriel Peyré
2014	J	`jnl`	The Degrees of Freedom of Partly Smooth Regularizers. CoRR Samuel Vaiter, Charles-Alban Deledalle, Gabriel Peyré, Jalal Fadili, Charles Dossal
2013	J	`jnl`	Model Selection with Piecewise Regular Gauges. CoRR Samuel Vaiter, Mohammad Golbabaee, Jalal Fadili, Gabriel Peyré
2013	J	`jnl`	Robust Polyhedral Regularization CoRR Samuel Vaiter, Gabriel Peyré, Jalal Fadili
2013	J	`jnl`	Robust Sparse Analysis Regularization. IEEE Trans. Inf. Theory Samuel Vaiter, Gabriel Peyré, Charles Dossal, Jalal Fadili
2013	J	`jnl`	Stable Recovery with Analysis Decomposable Priors CoRR Mohamed-Jalal Fadili, Gabriel Peyré, Samuel Vaiter, Charles-Alban Deledalle, Joseph Salmon
2012	J	`jnl`	Risk estimation for matrix recovery with spectral regularization CoRR Charles-Alban Deledalle, Samuel Vaiter, Gabriel Peyré, Jalal Fadili, Charles Dossal
2012	J	`jnl`	The degrees of freedom of the Group Lasso for a General Design CoRR Samuel Vaiter, Charles-Alban Deledalle, Gabriel Peyré, Jalal Fadili, Charles Dossal
2012	B	`conf`	Unbiased risk estimation for sparse analysis regularization. ICIP Charles-Alban Deledalle, Samuel Vaiter, Gabriel Peyré, Jalal Fadili, Charles Dossal
2011	J	`jnl`	Robust Sparse Analysis Regularization CoRR Samuel Vaiter, Gabriel Peyré, Charles Dossal, Jalal Fadili