Pauwels, Edouard

14 publications

TMLR 2025 A Second-Order-like Optimizer with Adaptive Gradient Scaling for Deep Learning Jerome Bolte, Ryan Boustany, Edouard Pauwels, Andrei Purica

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

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

ICLR 2023 On the Complexity of Nonsmooth Automatic Differentiation Jerome Bolte, Ryan Boustany, Edouard Pauwels, Béatrice Pesquet-Popescu

NeurIPS 2023 One-Step Differentiation of Iterative Algorithms Jerome Bolte, Edouard Pauwels, Samuel Vaiter

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

JMLR 2021 An Inertial Newton Algorithm for Deep Learning Camille Castera, Jérôme Bolte, Cédric Févotte, Edouard Pauwels

NeurIPS 2021 Nonsmooth Implicit Differentiation for Machine-Learning and Optimization Jérôme Bolte, Tam Le, Edouard Pauwels, Tony Silveti-Falls

NeurIPS 2021 Numerical Influence of ReLU’(0) on Backpropagation David Bertoin, Jérôme Bolte, Sébastien Gerchinovitz, Edouard Pauwels

NeurIPS 2021 Semialgebraic Representation of Monotone Deep Equilibrium Models and Applications to Certification Tong Chen, Jean B Lasserre, Victor Magron, Edouard Pauwels

NeurIPS 2020 A Mathematical Model for Automatic Differentiation in Machine Learning Jérôme Bolte, Edouard Pauwels

NeurIPS 2020 Semialgebraic Optimization for Lipschitz Constants of ReLU Networks Tong Chen, Jean B Lasserre, Victor Magron, Edouard Pauwels

NeurIPS 2018 Relating Leverage Scores and Density Using Regularized Christoffel Functions Edouard Pauwels, Francis Bach, Jean-Philippe Vert

NeurIPS 2016 Sorting Out Typicality with the Inverse Moment Matrix SOS Polynomial Edouard Pauwels, Jean B Lasserre