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

Recent Papers.

  1. 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]
  2. Braun, G., Carderera, A., Combettes, C. W., Hassani, H., Karbasi, A., Mokthari, A., and Pokutta, S. (2022). Conditional Gradient Methods. Preprint.
  3. Hendrych, D., Troppens, H., Besançon, M., and Pokutta, S. (2022). Convex integer optimization with Frank-Wolfe methods. Preprint. [arXiv] [slides] [code]
  4. Wirth, E., Kera, H., and Pokutta, S. (2022). Approximate Vanishing Ideal Computations at Scale. Preprint. [arXiv] [slides]
  5. Parczyk, O., Pokutta, S., Spiegel, C., and Szabó, T. (2022). New Ramsey Multiplicity Bounds and Search Heuristics. Preprint. [arXiv] [slides]
  6. Wäldchen, S., Sharma, K., Zimmer, M., and Pokutta, S. (2022). Merlin-Arthur Classifiers: Formal Interpretability with Interactive Black Boxes. Preprint. [arXiv]
  7. 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] [poster] [video]
  8. Tsuji, K., Tanaka, K., and Pokutta, S. (2022). Pairwise Conditional Gradients without Swap Steps and Sparser Kernel Herding. To Appear in Proceedings of ICML. [arXiv] [summary] [slides] [code] [video]
  9. Zimmer, M., Spiegel, C., and Pokutta, S. (2022). Compression-aware Training of Neural Networks using Frank-Wolfe. Preprint. [arXiv]
  10. Gelß, P., Klus, S., Shakibaei, Z., and Pokutta, S. (2022). Low-rank tensor decompositions of quantum circuits. Preprint. [arXiv]
  11. Deza, A., Pokutta, S., and Pournin, L. (2022). The complexity of geometric scaling. Preprint. [arXiv]
  12. 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] [poster] [video]
  13. Hunkenschröder, C., Pokutta, S., and Weismantel, R. (2022). Optimizing a low-dimensional convex function over a high-dimensional cube. Preprint. [arXiv]
  14. 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]
  15. 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]
  16. Kerdreux, T., Scieur, D., d’Aspremont, A., and Pokutta, S. (2022). Strong Convexity of Feasible Sets in Riemannian Manifolds. Preprint.
  17. Wirth, E., Kerdreux, T., and Pokutta, S. (2022). Acceleration of Frank-Wolfe algorithms with open loop step-sizes. Preprint. [arXiv]
  18. Wirth, E., and Pokutta, S. (2022). Conditional Gradients for the Approximately Vanishing Ideal. To Appear in Proceedings of AISTATS. [arXiv] [summary] [poster] [code]
  19. Roux, C., Wirth, E., Pokutta, S., and Kerdreux, T. (2021). Efficient Online-Bandit Strategies for Minimax Learning Problems. Preprint. [arXiv]
  20. Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2021). Local and Global Uniform Convexity Conditions. Preprint. [arXiv]
  21. Braun, G., and Pokutta, S. (2021). Dual Prices for Frank-Wolfe Algorithms. Preprint. [arXiv]

Select Recent Talks and Teaching.

Recent Blog Posts.


  • 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)
  • 06/2022: Interview on using AI to combat and mitigate climate change (German) [article] [magazine]
  • 10/2021: Math+ Cluster presentation at the Humboldt Forum “Mit Mathematik die Welt verbessern?” (German) [video]