On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent. - Scott Pesme; Aymeric Dieuleveut; Nicolas Flammarion (ICML, 2020)

Online Robust Regression via SGD on the l1 loss. - Scott Pesme; Nicolas Flammarion (NeurIPS, 2020)

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity. - Scott Pesme; Loucas Pillaud-Vivien; Nicolas Flammarion (NeurIPS, 2021)

(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability. - Mathieu Even; Scott Pesme; Suriya Gunasekar; Nicolas Flammarion (NeurIPS, 2023)

Saddle-to-Saddle Dynamics in Diagonal Linear Networks. - Scott Pesme; Nicolas Flammarion (NeurIPS, 2023)

(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability. - Mathieu Even; Scott Pesme; Suriya Gunasekar; Nicolas Flammarion (CoRR, 2023)

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks. - Hristo Papazov; Scott Pesme; Nicolas Flammarion (AISTATS, 2024)

Implicit Bias of Mirror Flow on Separable Data. - Scott Pesme; Radu-Alexandru Dragomir; Nicolas Flammarion (NeurIPS, 2024)

A Theoretical Framework for Grokking: Interpolation followed by Riemannian Norm Minimisation. - Etienne Boursier; Scott Pesme; Radu-Alexandru Dragomir (CoRR, 2025)

MAP Estimation with Denoisers: Convergence Rates and Guarantees. - Scott Pesme; Giacomo Meanti; Michael Arbel; Julien Mairal (CoRR, 2025)