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. Zimmer, M., Spiegel, C., and Pokutta, S. (2023). How I Learned to Stop Worrying and Love Retraining. To Appear in Proceedings of ICLR. [arXiv] [code]
2. Chmiela, A., Gleixner, A., Lichocki, P., and Pokutta, S. (2023). Online Learning for Scheduling MIP Heuristics. To Appear in Proceedings of CPAIOR.
3. Wirth, E., Kera, H., and Pokutta, S. (2023). Approximate Vanishing Ideal Computations at Scale. To Appear in Proceedings of ICLR. [arXiv] [slides]
4. Wirth, E., Kerdreux, T., and Pokutta, S. (2023). Acceleration of Frank-Wolfe algorithms with open loop step-sizes. To Appear in Proceedings of AISTATS. [arXiv]
5. Braun, G., Pokutta, S., and Weismantel, R. (2022). Alternating Linear Minimization: Revisiting von Neumann’s alternating projections. Preprint. [arXiv] [slides]
6. Braun, G., Carderera, A., Combettes, C. W., Hassani, H., Karbasi, A., Mokthari, A., and Pokutta, S. (2022). Conditional Gradient Methods. Preprint. [arXiv]
7. Parczyk, O., Pokutta, S., Spiegel, C., and Szabó, T. (2022). Fully Computer-Assisted Proofs in Extremal Combinatorics. To Appear in Proceedings of AAAI. [arXiv] [slides]
8. Martinez-Rubio, D., and Pokutta, S. (2022). Accelerated Riemannian Optimization: Handling Constraints to Bound Geometric Penalties. To Appear in NeurIPS OPT 2022 Workshop. [arXiv] [poster]
9. Criado, F., Martinez-Rubio, D., and Pokutta, S. (2022). Fast Algorithms for Packing Proportional Fairness and its Dual. To Appear in Proceedings of NeurIPS. [arXiv] [poster]
10. Hendrych, D., Troppens, H., Besançon, M., and Pokutta, S. (2022). Convex integer optimization with Frank-Wolfe methods. Preprint. [arXiv] [slides] [code]
11. Wäldchen, S., Sharma, K., Zimmer, M., and Pokutta, S. (2022). Merlin-Arthur Classifiers: Formal Interpretability with Interactive Black Boxes. Preprint. [arXiv]
12. Parczyk, O., Pokutta, S., Spiegel, C., and Szabó, T. (2022). New Ramsey Multiplicity Bounds and Search Heuristics. Preprint. [arXiv] [slides]
13. Zimmer, M., Spiegel, C., and Pokutta, S. (2022). Compression-aware Training of Neural Networks using Frank-Wolfe. Preprint. [arXiv]
14. Gelß, P., Klus, S., Shakibaei, Z., and Pokutta, S. (2022). Low-rank tensor decompositions of quantum circuits. Preprint. [arXiv]
15. Deza, A., Pokutta, S., and Pournin, L. (2022). The complexity of geometric scaling. Preprint. [arXiv]
16. Kerdreux, T., Scieur, D., d’Aspremont, A., and Pokutta, S. (2022). Strong Convexity of Feasible Sets in Riemannian Manifolds. Preprint.

Select Recent Talks and Teaching.

• 12/2022: (technical) “Alternating Linear Minimization: Revisiting von Neumann’s alternating projections”. Talk at Workshop on Recent Advances in Optimization (Toronto, Canada). [slides]
• 09/2022: (technical) “Convex integer optimization with Frank-Wolfe methods”. Talk at Advances in Classical and Quantum Algorithms for Optimization and Machine Learning (Tokyo, Japan). [slides]
• 07/2022: (technical) “Structured ML Training via Conditional Gradients”. Plenary at Workshop on Algorithmic Optimization and Data Science (Trier, Germany). [slides]
• 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]
• SoSe/2021: Discrete Optimization and Machine Learning (seminar)

Recent Blog Posts.

• 12/2022: Sh**t you can do with the euclidean norm
• 11/2022: Monograph on Conditional Gradients and Frank-Wolfe methods
• 08/2022: Boscia.jl - a new Mixed-Integer Convex Programming (MICP) solver
• 07/2022: Acceleration of Frank-Wolfe algorithms with open loop step-sizes
• 05/2022: Pairwise Conditional Gradients without Swap Steps

News.

• 02/2023: We are organizing a Thematic Einstein Semester on “Mathematical Optimization for Machine Learning” within the Math+ Cluster of Excellence. The semester consists of various activities throughout the semester with three workshops, a conference, and a summer school as some of the highlights. We are looking forward to seeing you in Berlin!
• 11/2022: We finished our monograph on Frank-Wolfe methods a.k.a. Conditional Gradients. [arxiv] [webpage] [blog]
• 06/2022: Symposium on Theory of Computing (STOC) Test of Time award (10 years) for “Linear vs. Semidefinite Extended Formulations: Exponential Separation and Strong Lower Bounds”, S. Fiorini, S. Massar, S. Pokutta, H.R. Tiwary, R. de Wolf from 2012.
• 06/2022: 6th RIKEN-IMI-ISM-ZIB-MODAL-NHR Workshop on Advances in Classical and Quantum Algorithms for Optimization and Machine Learning in Japan. [link]
• 06/2022: New ZIB videos available. [youtube channel]. (german only)