Christophe Roux
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.
📬 Contact
- office
- Room 3107 at ZIB
- roux (at) zib.de
christophe.roux (at) campus.tu-berlin.de - homepage
- christopheroux.de
- languages
- German, English, and French
🎓 Curriculum vitae
- since 2022
- Member of BMS
- since 2021
- Researcher at ZIB
- 2021 to 2021
- Research Assistant at ZIB
- Jun 2021
- M.Sc. in Scientific Computing at TUB
- Feb 2018
- B.Sc. in Engineering Science at TUB
đź“ť Publications and preprints
Preprints
- Roux, C., Zimmer, M., and Pokutta, S. (2024). On the Byzantine-resilience of Distillation-based Federated Learning.
[arXiv]
[BibTeX]
- MartĂnez-Rubio, D., Roux, C., Criscitiello, C., and Pokutta, S. (2023). Accelerated Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties.
[arXiv]
[BibTeX]
- Roux, C., Wirth, E., Pokutta, S., and Kerdreux, T. (2021). Efficient Online-bandit Strategies for Minimax Learning Problems.
[arXiv]
[BibTeX]
Conference proceedings
- MartĂnez-Rubio, D., Roux, C., and Pokutta, S. (2024). Convergence and Trade-offs in Riemannian Gradient Descent and Riemannian Proximal Point. Proceedings of the International Conference on Machine Learning.
[URL]
[arXiv]
[BibTeX]
Full articles
🔬 Projects
SynLab researches mathematical generalization of application-specific advances achieved in the Gas-, Rail– and MedLab of the research campus MODAL. The focus is on exact methods for solving a broad class of discrete-continuous optimization problems. This requires advanced techniques for structure recognition, consideration of nonlinear restrictions from practice, and the efficient implementation of mathematical algorithms on modern computer architectures. The results are bundled in a professional software package and complemented by a range of high-performance methods for specific applications with a high degree of innovation.
đź’¬ Talks and posters
Conference and workshop talks
- Nov 2023
- Bounding Geometric Penalties in First-order Riemannian Optimization
Seminar "Modern Methods in Applied Stochastics and Nonparametric Statistics" - Mar 2023
- Riemannian Optimization: How and Why?
Workshop on Optimization and Machine Learning, Waischenfeld
Research seminar talks
- Mar 2025
- TBD
IOL Research Seminar (IOL), Berlin - Apr 2024
- Bounding Geometric Penalties in Riemannian Optimization
CISPA Research seminar, SaarbrĂĽcken
Poster presentations
- Jul 2024
- Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point
41st International Conference on Machine Learning (ICML), Vienna - Mar 2023
- Accelerated Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties
Workshop on Optimization and Machine Learning, Waischenfeld
đź“… Event Attendance
- May 2025
- 7th DOxML Conference, Kyoto
- Jul 2024
- 41st International Conference on Machine Learning (ICML), Vienna
- Dec 2023
- Neurips@Paris