Vaiter, Samuel

20 publications

NeurIPS 2025 Differentiable Generalized Sliced Wasserstein Plans Laetitia Chapel, Romain Tavenard, Samuel Vaiter

NeurIPS 2025 From Shortcut to Induction Head: How Data Diversity Shapes Algorithm Selection in Transformers Ryotaro Kawata, Yujin Song, Alberto Bietti, Naoki Nishikawa, Taiji Suzuki, Samuel Vaiter, Denny Wu

NeurIPS 2025 Learning Theory for Kernel Bilevel Optimization Fares El Khoury, Edouard Pauwels, Samuel Vaiter, Michael Arbel

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

JMLR 2024 Convergence of Message-Passing Graph Neural Networks with Generic Aggregation on Large Random Graphs Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter

NeurIPS 2024 Derivatives of Stochastic Gradient Descent in Parametric Optimization Franck Iutzeler, Edouard Pauwels, Samuel Vaiter

TMLR 2024 Gradient Scarcity in Graph Learning with Bilevel Optimization Hashem Ghanem, Samuel Vaiter, Nicolas Keriven

AISTATS 2024 Provable Local Learning Rule by Expert Aggregation for a Hawkes Network Sophie Jaffard, Samuel Vaiter, Alexandre Muzy, Patricia Reynaud-Bouret

ICML 2023 On the Robustness of Text Vectorizers Rémi Catellier, Samuel Vaiter, Damien Garreau

NeurIPS 2023 One-Step Differentiation of Iterative Algorithms Jerome 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 2022 A Framework for Bilevel Optimization That Enables Stochastic and Global Variance Reduction Algorithms Mathieu Dagréou, Pierre Ablin, Samuel Vaiter, Thomas Moreau

NeurIPS 2022 Automatic Differentiation of Nonsmooth Iterative Algorithms Jerome Bolte, Edouard Pauwels, Samuel Vaiter

NeurIPS 2022 Benchopt: Reproducible, Efficient and Collaborative Optimization Benchmarks Thomas Moreau, Mathurin Massias, Alexandre Gramfort, Pierre Ablin, Pierre-Antoine Bannier, Benjamin Charlier, Mathieu Dagréou, Tom Dupre la Tour, Ghislain Durif, Cassio 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

JMLR 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

MLJ 2021 Linear Support Vector Regression with Linear Constraints Quentin Klopfenstein, Samuel Vaiter

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

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

JMLR 2020 Dual Extrapolation for Sparse GLMs Mathurin Massias, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon

ICML 2020 Implicit Differentiation of Lasso-Type Models for Hyperparameter Optimization Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon