Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction. - Radu-Alexandru Dragomir; Mathieu Even; Hadrien Hendrikx (ICML, 2021)

Quartic First-Order Methods for Low-Rank Minimization. - Radu-Alexandru Dragomir; Alexandre d'Aspremont; Jérôme Bolte (J. Optim. Theory Appl., 2021)

Optimal complexity and certification of Bregman first-order methods. - Radu-Alexandru Dragomir; Adrien B. Taylor; Alexandre d'Aspremont; Jérôme Bolte (Math. Program., 2022)

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

Convex Quartic Problems: Homogenized Gradient Method and Preconditioning. - Radu-Alexandru Dragomir; Yurii E. Nesterov (SIAM J. Optim., 2025)

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