Learning with Random Learning Rates. - Léonard Blier; Pierre Wolinski; Yann Ollivier (ECML/PKDD (2), 2019)

Interpreting a Penalty as the Influence of a Bayesian Prior. - Pierre Wolinski; Guillaume Charpiat; Yann Ollivier (CoRR, 2020)

Structural Learning of Neural Networks. (Apprentissage de structure pour les réseaux de neurones). - Pierre Wolinski (2020)

Imposing Gaussian Pre-Activations in a Neural Network. - Pierre Wolinski; Julyan Arbel (CoRR, 2022)

Rethinking Gauss-Newton for learning over-parameterized models. - Michael Arbel; Romain Menegaux; Pierre Wolinski (NeurIPS, 2023)

Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review. - Minh Tri Le; Pierre Wolinski; Julyan Arbel (CoRR, 2023)

Adapting Newton's Method to Neural Networks through a Summary of Higher-Order Derivatives. - Pierre Wolinski (CoRR, 2023)

Gaussian Pre-Activations in Neural Networks: Myth or Reality? - Pierre Wolinski; Julyan Arbel (Trans. Mach. Learn. Res., 2025)