Gradient based sample selection for online continual learning. - Rahaf Aljundi; Min Lin; Baptiste Goujaud; Yoshua Bengio (NeurIPS, 2019)

Online continual learning with no task boundaries. - Rahaf Aljundi; Min Lin; Baptiste Goujaud; Yoshua Bengio (CoRR, 2019)

A Study of Condition Numbers for First-Order Optimization. - Charles Guille-Escuret; Manuela Girotti; Baptiste Goujaud; Ioannis Mitliagkas (AISTATS, 2021)

Super-Acceleration with Cyclical Step-sizes. - Baptiste Goujaud; Damien Scieur; Aymeric Dieuleveut; Adrien B. Taylor; Fabian Pedregosa (AISTATS, 2022)

Gradient Descent Is Optimal Under Lower Restricted Secant Inequality And Upper Error Bound. - Charles Guille-Escuret; Adam Ibrahim; Baptiste Goujaud; Ioannis Mitliagkas (NeurIPS, 2022)

On Fundamental Proof Structures in First-Order Optimization. - Baptiste Goujaud; Aymeric Dieuleveut; Adrien B. Taylor (CDC, 2023)

Counter-Examples in First-Order Optimization: A Constructive Approach. - Baptiste Goujaud; Aymeric Dieuleveut; Adrien B. Taylor (IEEE Control. Syst. Lett., 2023)

Proving Linear Mode Connectivity of Neural Networks via Optimal Transport. - Damien Ferbach; Baptiste Goujaud; Gauthier Gidel; Aymeric Dieuleveut (AISTATS, 2024)

Short Paper - Quadratic minimization: from conjugate gradient to an adaptive Polyak's momentum method with Polyak step-sizes. - Baptiste Goujaud; Adrien B. Taylor; Aymeric Dieuleveut (Open J. Math. Optim., 2024)

PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python. - Baptiste Goujaud; Céline Moucer; François Glineur; Julien M. Hendrickx; Adrien B. Taylor; Aymeric Dieuleveut (Math. Program. Comput., 2024)

Gradient Descent on Logistic Regression: Do Large Step-Sizes Work with Data on the Sphere? - Si Yi Meng; Baptiste Goujaud; Antonio Orvieto; Christopher De Sa (CoRR, 2025)