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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
- 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., MartĂnez-Rubio, D., and Pokutta, S. (2025). Implicit Riemannian Optimism with Applications to Min-Max Problems.
[arXiv]
[BibTeX]
@misc{2025_RouxMartinezrubioPokutta_ImplicitRiemannian, archiveprefix = {arXiv}, eprint = {2501.18381}, primaryclass = {math.OC}, year = {2025}, author = {Roux, Christophe and MartĂnez-Rubio, David and Pokutta, Sebastian}, title = {Implicit Riemannian Optimism with Applications to Min-Max Problems}, date = {2025-01-30} }
- Roux, C., Wirth, E., Pokutta, S., and Kerdreux, T. (2021). Efficient Online-bandit Strategies for Minimax Learning Problems.
[arXiv]
[BibTeX]
@misc{2021_RouxWirthPokuttaKerdreux_Onlinebanditminimax, archiveprefix = {arXiv}, eprint = {2105.13939}, primaryclass = {cs.LG}, year = {2021}, author = {Roux, Christophe and Wirth, Elias and Pokutta, Sebastian and Kerdreux, Thomas}, title = {Efficient Online-bandit Strategies for Minimax Learning Problems}, date = {2021-05-28} }
Conference proceedings
- MartĂnez-Rubio, D., Roux, C., Criscitiello, C., and Pokutta, S. (2023, May 25). Accelerated Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties. Proceedings of the International Conference on Artificial Intelligence and Statistics.
[arXiv]
[BibTeX]
@inproceedings{2023_MartinezrubioRouxCriscitielloPokutta_Riemannianminmax, year = {2025}, booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics}, archiveprefix = {arXiv}, eprint = {2305.16186}, primaryclass = {math.OC}, author = {MartĂnez-Rubio, David and Roux, Christophe and Criscitiello, Christopher and Pokutta, Sebastian}, title = {Accelerated Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties}, date = {2023-05-25} }
- Roux, C., Zimmer, M., and Pokutta, S. (2024, February 19). On the Byzantine-resilience of Distillation-based Federated Learning. Proceedings of the International Conference on Learning Representations.
[arXiv]
[BibTeX]
@inproceedings{2024_RouxZimmerPokutta_Byzantineresilience, year = {2025}, booktitle = {Proceedings of the International Conference on Learning Representations}, archiveprefix = {arXiv}, eprint = {2402.12265}, primaryclass = {cs.LG}, author = {Roux, Christophe and Zimmer, Max and Pokutta, Sebastian}, title = {On the Byzantine-resilience of Distillation-based Federated Learning}, date = {2024-02-19} }
- MartĂnez-Rubio, D., Roux, C., and Pokutta, S. (2024, March 15). Convergence and Trade-offs in Riemannian Gradient Descent and Riemannian Proximal Point. Proceedings of the International Conference on Machine Learning.
[URL]
[arXiv]
[BibTeX]
@inproceedings{2024_MartinezrubioRouxPokutta_Riemanniangradientdescent, year = {2024}, booktitle = {Proceedings of the International Conference on Machine Learning}, url = {https://proceedings.mlr.press/v235/marti-nez-rubio24a.html}, archiveprefix = {arXiv}, eprint = {2403.10429}, primaryclass = {math.OC}, author = {MartĂnez-Rubio, David and Roux, Christophe and Pokutta, Sebastian}, title = {Convergence and Trade-offs in Riemannian Gradient Descent and Riemannian Proximal Point}, date = {2024-03-15} }
Full articles
- Kerdreux, T., Roux, C., d’Aspremont, A., and Pokutta, S. (2021). Linear Bandits on Uniformly Convex Sets. Journal of Machine Learning Research, 22(284), 1–23.
[URL]
[arXiv]
[summary]
[BibTeX]
@article{2021_KerdreuxRouxDaspremontPokutta_Linearbandits, year = {2021}, journal = {Journal of Machine Learning Research}, month = mar, volume = {22}, number = {284}, pages = {1–23}, url = {http://jmlr.org/papers/v22/21-0277.html}, archiveprefix = {arXiv}, eprint = {2103.05907}, primaryclass = {cs.LG}, author = {Kerdreux, Thomas and Roux, Christophe and d'Aspremont, Alexandre and Pokutta, Sebastian}, title = {Linear Bandits on Uniformly Convex Sets}, summary = {https://www.pokutta.com/blog/research/2021/04/03/linearBandits.html}, date = {2021-03-10} }
🔬 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
- Bounding Geometric Penalties in Riemannian Optimization
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
- Mar 2023
- Workshop on Optimization and Machine Learning, Waischenfeld