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 under the supervision of Sebastian Pokutta. I am also a member of the Berlin Mathematical School (BMS).

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

E-mail: roux@zib.de

Publications

* means equal contribution

1. Roux*, C., Martínez-Rubio*, D., C., Pokutta, S. (2025). Implicit Riemannian Optimism with Applications to Min-Max Problems. To Appear in Proceedings of ICML 2025. [arXiv]​
2. Martínez-Rubio*, D., Roux*, C., Criscitiello, C., Pokutta, S. (2025). Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, PMLR 258:280-288. [AISTATS]​[arXiv]​
3. Roux*,C., Zimmer*,M., Pokutta, S. (2025). On the Byzantine-Resilience of Distillation-Based Federated Learning. To Appear in Proceedings of ICLR 2025. [arXiv]​
4. Martínez-Rubio*, D., Roux*, C., Pokutta, S. (2024). Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point. Proceedings of the 41st International Conference on Machine Learning, PMLR 235:34920-34948 [ICML] [arXiv]​
5. Roux, C., Wirth, E., Pokutta, S., and Kerdreux, T. (2021). Efficient Online-Bandit Strategies for Minimax Learning Problems. Preprint. [arXiv]​
6. 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]​