Prof. Dr. Sebastian Pokutta

Vice President
Zuse Institute Berlin (ZIB)

Professor for
Optimization and Machine Learning
Mathematics and EECS (courtesy)
Technische Universität Berlin

Recent Papers.

  1. Designolle, S., Vértesi, T., and Pokutta, S. (2024). Symmetric multipartite Bell inequalities via Frank-Wolfe algorithms. Physical Review A, 109(2). [PDF] [arXiv]
  2. Hendrych, D., Besançon, M., and Pokutta, S. (2024). Solving the Optimal Experiment Design Problem with Mixed-Integer Convex Methods. To Appear in Proceedings of Symposium on Experimental Algorithms. [arXiv]
  3. Carderera, A., Besançon, M., and Pokutta, S. (2024). Scalable Frank-Wolfe on Generalized Self-Concordant Functions via Simple Steps. To Appear in SIAM Journal on Optimization. [arXiv] [summary] [slides] [poster] [code]
  4. Mundinger, K., Pokutta, S., Spiegel, C., and Zimmer, M. (2024). Extending the Continuum of Six-Colorings. Preprint. [arXiv]
  5. Mundinger, K., Zimmer, M., and Pokutta, S. (2024). Neural Parameter Regression for Explicit Representations of PDE Solution Operators. To Appear in AI4DiffEqtnsInSci @ ICLR 2024 Workshop. [arXiv]
  6. Göß, A., Martin, A., Pokutta, S., and Sharma, K. (2024). Norm-induced Cuts: Optimization with Lipschitzian Black-box Functions. Preprint. [arXiv]
  7. Martinez-Rubio, D., Roux, C., and Pokutta, S. (2024). Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point. Preprint. [arXiv]
  8. Roux, C., Zimmer, M., and Pokutta, S. (2024). On the Byzantine-Resilience of Distillation-Based Federated Learning. Preprint. [arXiv]
  9. Wäldchen, S., Sharma, K., Turan, B., Zimmer, M., and Pokutta, S. (2024). Interpretability Guarantees with Merlin-Arthur Classifiers. To Appear in Proceedings of AISTATS. [arXiv]
  10. Sharma, K., Hendrych, D., Besançon, M., and Pokutta, S. (2024). Network Design for the Traffic Assignment Problem with Mixed-Integer Frank-Wolfe. Preprint. [arXiv] [code]
  11. Pokutta, S. (2024). The Frank-Wolfe algorithm: a short introduction. Jahresbericht Der Deutschen Mathematiker-Vereinigung, 126, 3–35. [PDF] [arXiv]
  12. Zimmer, M., Spiegel, C., and Pokutta, S. (2024). Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging. To Appear in Proceedings of ICLR. [arXiv]
  13. Zimmer, M., Andoni, M., Spiegel, C., and Pokutta, S. (2023). PERP: Rethinking the Prune-Retrain Paradigm in the Era of LLMs. Preprint. [arXiv] [code]
  14. 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. (2023). Quantum Optimization: Potential, Challenges, and the Path Forward. Preprint. [arXiv]
  15. Kiem, A., Pokutta, S., and Spiegel, C. (2023). The Four-Color Ramsey Multiplicity of Triangles. Preprint. [arXiv]
  16. Sadiku, S., Wagner, M., and Pokutta, S. (2023). Group-wise Sparse and Explainable Adversarial Attacks. Preprint. [arXiv]
  17. Woodstock, Z., and Pokutta, S. (2023). Splitting the Conditional Gradient Algorithm. Preprint. [arXiv]
  18. Wirth, E., Peña, J., and Pokutta, S. (2023). Accelerated Affine-Invariant Convergence Rates of the Frank-Wolfe Algorithm with Open-Loop Step-Sizes. Preprint. [arXiv]
  19. Stengl, M., Gelß, P., Klus, S., and Pokutta, S. (2023). Existence and Uniqueness of Solutions of the Koopman–von Neumann Equation on Bounded Domains. Preprint. [arXiv]
  20. Deza, A., Onn, S., Pokutta, S., and Pournin, L. (2023). Kissing polytopes. Preprint. [arXiv]
  21. Braun, G., Carderera, A., Combettes, C. W., Hassani, H., Karbasi, A., Mokthari, A., and Pokutta, S. (2022). Conditional Gradient Methods. Preprint. [arXiv]

Select Recent Talks and Teaching.

Recent Blog Posts.


  • 01/2024: The Zuse Institute Berlin (ZIB) is celebrating its 40th anniversary in 2024.
  • 05/2023: Received Gödel Prize together with Samuel Fiorini, Serge Massar, Hans Raj Tiwary, Ronald de Wolf, and Thomas Rothvoss.
  • 05/2023: We are organizing the fifth conference on “Discrete Optimization and Machine Learning” in Aug 2023 at GRIPS in Tokyo.
  • 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]