I am a doctoral researcher at Zuse Institute Berlin (ZIB) in the Interactive Optimization and Learning (IOL) research group and a PhD candidate at the Institute of Mathematics, TU Berlin. I am also a member of the Berlin Mathematical School (BMS).

My research interests lie at the intersection of optimization and machine learning. In particular, I am interested in online optimization, federated learning and optimization on riemannian manifolds.

E-mail: roux@zib.de

Publications

* means equal contribution

1. Martínez-Rubio*, D., Roux*, C., Pokutta, S. (2024). Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point. Preprint. [arXiv]​
2. Roux*,C., Zimmer*,M., Pokutta, S. (2024). On the Byzantine-Resilience of Distillation-Based Federated Learning. Preprint. [arXiv]​
3. Martínez-Rubio*, D., Roux*, C., Criscitiello, C., Pokutta, S. (2023). Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties. Preprint. [arXiv]​
4. Roux, C., Wirth, E., Pokutta, S., and Kerdreux, T. (2021). Efficient Online-Bandit Strategies for Minimax Learning Problems. Preprint. [arXiv]​
5. Kerdreux, T., Roux, C., d’Aspremont, A., and Pokutta, S. (2021). Linear Bandits on Uniformly Convex Sets. Journal of Machine Learning Research (JMLR). [JMLR] [arXiv]​ [summary]​