Sebastian Pokutta

Artificial Intelligence, Optimization, and Machine Learning. Professor @TUBerlin, Vice President @ZuseInstitute

Research Lab

My group is interested in Artificial Intelligence, Optimization, and Machine Learning and its applications. 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. Recent examples include the estimation of biomass from satellite data, entanglement and non-locality thresholds via optimization, new constructions in extremal combinatorics via AI, as well as questions around AI and creativity. [group homepage] [more about research and projects]

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 check our openings.

Select Recent Papers

  1. Wirth, E., Peña, J., and Pokutta, S. (2024). Accelerated Affine-Invariant Convergence Rates of the Frank-Wolfe Algorithm with Open-Loop Step-Sizes. To Appear in Mathematical Programming A. [arXiv]
  2. Vu-Han, T.-L., Sunkara, V., Bermudez-Schettino, R., Schwechten, J., Runge, R., Perka, C., Winkler, T., Pokutta, S., Weiß, C., and Pumberger, M. (2024). Feature Engineering for the Prediction of Scoliosis in 5q-Spinal Muscular Atrophy. Journal of Cachexia, Sarcopenia and Muscle. [PDF]
  3. Haase, J., and Pokutta, S. (2024). Human-AI Co-Creativity: Exploring Synergies Across Levels of Creative Collaboration. Preprint. [arXiv]
  4. Sadiku, S., Wagner, M., Nagarajan, S. G., and Pokutta, S. (2024). S-CFE: Simple Counterfactual Explanations. Preprint. [arXiv]
  5. Głuch, G., Turan, B., Nagarajan, S. G., and Pokutta, S. (2024). The Good, the Bad and the Ugly: Watermarks, Transferable Attacks and Adversarial Defenses. Preprint. [arXiv] [summary] [poster]
  6. Designolle, S., Vértesi, T., and Pokutta, S. (2024). Better bounds on Grothendieck constants of finite orders. Preprint. [arXiv]
  7. Braun, G., Pokutta, S., and Woodstock, Z. (2024). Flexible block-iterative analysis for the Frank-Wolfe algorithm. Preprint. [arXiv]
  8. Abbas, A., Ambainis, A., Augustino, B., Bärtschi, A., Buhrman, H., Coffrin, C., Cortiana, G., Dunjko, V., Egger, D. J., Elmegreen, B. G., Franco, N., Fratini, F., Fuller, B., Gacon, J., Gonciulea, C., Gribling, S., Gupta, S., Hadfield, S., Heese, R., … Zoufal, C. (2024). Quantum Optimization: Potential, Challenges, and the Path Forward. Nature Reviews Physics. [PDF] [arXiv]
  9. Stengl, M., Gelß, P., Klus, S., and Pokutta, S. (2024). Existence and Uniqueness of Solutions of the Koopman–von Neumann Equation on Bounded Domains. To Appear in Journal of Physics A. [arXiv]
  10. Wirth, E., Besançon, M., and Pokutta, S. (2024). The Pivoting Framework: Frank-Wolfe Algorithms with Active Set Size Control. Preprint. [arXiv]
  11. Deza, A., Onn, S., Pokutta, S., and Pournin, L. (2024). Kissing polytopes. To Appear in SIAM Journal on Discrete Mathematics. [PDF] [arXiv]
  12. Wirth, E., Peña, J., and Pokutta, S. (2024). Fast Convergence of Frank-Wolfe algorithms on polytopes. Preprint. [arXiv]
  13. Martinez-Rubio, D., Roux, C., and Pokutta, S. (2024). Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point. Proceedings of ICML. [arXiv]
  14. Pauls, J., Zimmer, M., Kelly, U. M., Schwartz, M., Saatchi, S., Ciais, P., Pokutta, S., Brandt, M., and Gieseke, F. (2024). Estimating Canopy Height at Scale. Proceedings of ICML. [arXiv] [code]
  15. Kiem, A., Pokutta, S., and Spiegel, C. (2024). Categorification of Flag Algebras. Proceedings of Discrete Mathematics Days. [arXiv]
  16. Mundinger, K., Pokutta, S., Spiegel, C., and Zimmer, M. (2024). Extending the Continuum of Six-Colorings. Geombinatorics Quarterly. [arXiv] [summary] [slides]
  17. Mundinger, K., Zimmer, M., and Pokutta, S. (2024). Neural Parameter Regression for Explicit Representations of PDE Solution Operators. ICLR 2024 Workshop on AI4DifferentialEquations In Science. [PDF] [arXiv] [slides] [poster]
  18. Designolle, S., Vértesi, T., and Pokutta, S. (2024). Symmetric multipartite Bell inequalities via Frank-Wolfe algorithms. Physical Review A, 109(2). [PDF] [arXiv]
  19. Roux, C., Zimmer, M., and Pokutta, S. (2024). On the Byzantine-Resilience of Distillation-Based Federated Learning. Preprint. [arXiv] [summary] [code]
  20. Pokutta, S. (2024). The Frank-Wolfe algorithm: a short introduction. Jahresbericht Der Deutschen Mathematiker-Vereinigung, 126, 3–35. [PDF] [arXiv]
  21. Zimmer, M., Spiegel, C., and Pokutta, S. (2024). Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging. Proceedings of ICLR. [arXiv]
  22. Braun, G., Carderera, A., Combettes, C. W., Hassani, H., Karbasi, A., Mokthari, A., and Pokutta, S. (2022). Conditional Gradient Methods. Preprint. [arXiv]

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