Prof. Dr. Sebastian Pokutta

Vice President and Division Head
Mathematical Algorithmic Intelligence
AI in Society, Science, and Technology (AIS²T)
Zuse Institute Berlin (ZIB)

Professor for Optimization and Machine Learning
Institute of Mathematics
Electrical Engineering and Computer Science (courtesy)
Technische Universität Berlin

Research Lab. My group is interested in Artificial Intelligence, Optimization, and Machine Learning. We develop new methodologies (e.g., new optimization and learning algorithms), work on combining learning and decision-making, as well as design AI Systems for real-world deployment in various application contexts. [more]

(Informal) TL;DR. We use computers to learn from data and make better decisions.

Prospective Students. If you are interested in working in our group or writing your MS/BS thesis please only use the email applications-aisst@zib.de.

Recent Papers.

1. Tsuji, K., Tanaka, K., and Pokutta, S. (2022). Sparser Kernel Herding with Pairwise Conditional Gradients without Swap Steps. To Appear in Proceedings of ICML. [arXiv] [code]
2. Wäldchen, S., Huber, F., and Pokutta, S. (2022). Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four. To Appear in Proceedings of ICML. [arXiv]
3. Deza, A., Pokutta, S., and Pournin, L. (2022). The complexity of geometric scaling. Preprint. [arXiv]
4. Macdonald, J., Besançon, M., and Pokutta, S. (2022). Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings. To Appear in Proceedings of ICML. [arXiv]
5. Kossen, T., Hirzel, M. A., Madai, V. I., Boenisch, F., Hennemuth, A., Hildebrand, K., Pokutta, S., Sharma, K., Hilbert, A., Sobesky, J., Galinovic, I., Khalil, A. A., Fiebach, J. B., and Frey, D. (2022). Towards sharing brain images: Differentially private TOF-MRA images with segmentation labels using generative adversarial networks. Frontiers in Artificial Intelligence. [PDF]
6. Gasse, M., Cappart, Q., Charfreitag, J., Charlin, L., Chételat, D., Chmiela, A., Dumouchelle, J., Gleixner, A., Kazachkov, A. M., Khalil, E., Lichocki, P., Lodi, A., Lubin, M., Maddison, C. J., Morris, C., Papageorgiou, D. J., Parjadis, A., Pokutta, S., Prouvost, A., … Kun, M. (2022). The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights. Preprint. [arXiv]
7. Hunkenschröder, C., Pokutta, S., and Weismantel, R. (2022). Optimizing a low-dimensional convex function over a high-dimensional cube. Preprint. [arXiv]
8. Kerdreux, T., Scieur, D., d’Aspremont, A., and Pokutta, S. (2022). Strong Convexity of Feasible Sets in Riemannian Manifolds. Preprint.
9. Wirth, E., Kerdreux, T., and Pokutta, S. (2022). Acceleration of Frank-Wolfe algorithms with open loop step-sizes. Preprint.
10. Wirth, E., and Pokutta, S. (2022). Conditional Gradients for the Approximately Vanishing Ideal. To Appear in Proceedings of AISTATS. [arXiv] [summary] [poster] [code]
11. Zimmer, M., Spiegel, C., and Pokutta, S. (2021). How I Learned to Stop Worrying and Love Retraining. Preprint. [arXiv] [code]
12. Criado, F., Martinez-Rubio, D., and Pokutta, S. (2021). Fast Algorithms for Packing Proportional Fairness and its Dual. Preprint. [arXiv] [poster]
13. Roux, C., Wirth, E., Pokutta, S., and Kerdreux, T. (2021). Efficient Online-Bandit Strategies for Minimax Learning Problems. Preprint. [arXiv]
14. Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2021). Local and Global Uniform Convexity Conditions. Preprint. [arXiv]
15. Braun, G., and Pokutta, S. (2021). Dual Prices for Frank-Wolfe Algorithms. Preprint. [arXiv]

Select Recent Talks and Teaching.

• 04/2022: (technical) “Conditional Gradients in Machine Learning and Optimization”. Talk at IST ELLIS Seminar Series (Klosterneuburg, Austria). [slides]
• 11/2021: (technical) “Discrete Optimization in Machine Learning - an (informal) overview”. Talk at Oberwolfach Workshop on Combinatorial Optimization (Oberwolfach, Germany). [slides]
• 10/2021: (technical) “Fast algorithms for 1-fair packing (and its dual)”. Talk at HIM Workshop: Continuous approaches to discrete optimization (Bonn, Germany). [slides]
• 09/2021: (technical) “Conditional Gradients - a tour d’horizon”. Talk at AI Campus Berlin Tech Lunch Talk (online). [slides]
• 02/2021: (technical) “Structured ML Training via Conditional Gradients”. Talk at IPAM Deep Learning and Combinatorial Optimization Workshop (online). [slides] [video]
• SoSe/2021: Discrete Optimization and Machine Learning (seminar)

Recent Blog Posts.

• 05/2022: Quantum Computing for the Uninitiated: The Basics
• 02/2022: Conditional Gradients for the Approximately Vanishing Ideal
• 12/2021: Fast algorithms for fair packing and its dual
• 10/2021: Simple steps are all you need
• 06/2021: New(!!) NeurIPS 2021 competition: Machine Learning for Discrete Optimization (ML4CO)

News.

• 10/2021: Math+ Cluster presentation at the Humboldt Forum “Mit Mathematik die Welt verbessern?” (German) [video]
• 10/2021: Our group received a Google Research Award to explore the learning of heuristic schedules in MIP solvers.
• 10/2021: One project funded by the Math+ Research Center.
• 11/2020: Our group received a Google Research Award to support our work on Integer Programming solvers.
• 10/2020: Four projects funded by the Math+ Research Center.