Vice President
Zuse Institute Berlin (ZIB)
Professor for
Optimization and Machine Learning
Mathematics and EECS (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 about research and projects]
(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.
- Designolle, S., Vértesi, T., and Pokutta, S. (2024). Symmetric multipartite Bell inequalities via Frank-Wolfe algorithms. Physical Review A, 109(2). [PDF] [arXiv]
- Roux, C., Zimmer, M., and Pokutta, S. (2024). On the Byzantine-Resilience of Distillation-Based Federated Learning. Preprint. [arXiv]
- 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]
- 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]
- 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]
- 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]
- Pokutta, S. (2023). The Frank-Wolfe algorithm: a short introduction. Jahresbericht Der Deutschen Mathematiker-Vereinigung. [PDF] [arXiv]
- Hendrych, D., Besançon, M., and Pokutta, S. (2023). Solving the Optimal Experiment Design Problem with Mixed-Integer Convex Methods. Preprint. [arXiv]
- Zimmer, M., Andoni, M., Spiegel, C., and Pokutta, S. (2023). PERP: Rethinking the Prune-Retrain Paradigm in the Era of LLMs. Preprint. [arXiv] [code]
- Kiem, A., Pokutta, S., and Spiegel, C. (2023). The Four-Color Ramsey Multiplicity of Triangles. Preprint. [arXiv]
- Woodstock, Z., and Pokutta, S. (2023). Splitting the Conditional Gradient Algorithm. Preprint. [arXiv]
- Sadiku, S., Wagner, M., and Pokutta, S. (2023). Group-wise Sparse and Explainable Adversarial Attacks. Preprint. [arXiv]
- Thuerck, D., Sofranac, B., Pfetsch, M., and Pokutta, S. (2023). Learning Cuts via Enumeration Oracles. To Appear in Proceedings of NeurIPS. [arXiv]
- 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]
- Deza, A., Onn, S., Pokutta, S., and Pournin, L. (2023). Kissing polytopes. Preprint. [arXiv]
- 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]
- Martinez-Rubio, D., Roux, C., Criscitiello, C., and Pokutta, S. (2023). Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties. To Appear in NeurIPS OPT 2023 Workshop. [arXiv]
- Martinez-Rubio, D., Wirth, E., and Pokutta, S. (2023). Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond. Proceedings of COLT. [arXiv] [slides] [poster]
- Braun, G., Pokutta, S., and Weismantel, R. (2022). Alternating Linear Minimization: Revisiting von Neumann’s alternating projections. Preprint. [arXiv] [slides] [video]
- 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.
- 02/2024: (technical) “Conditional Gradients in Machine Learning”. Talk at NUS Math Seminar (Singapore). [slides]
- 11/2023: (technical) “Conditional Gradients in Machine Learning”. Keynote at Workshop on Geometry and Machine Learning (Leipzig, Germany). [slides]
- 09/2023: (technical) “The approximate Carathéodory problem and an application to quantum mechanics”. Plenary at 7th Workshop on Future Algorithms and Applications (Berlin, Germany).
- 08/2023: (technical) “Alternating Linear Minimization: Revisiting von Neumann’s alternating projections”. Talk at ICIAM 2023 (Tokyo, Japan). [slides]
- 08/2023: (technical) “Improved local models and new Bell inequalities via Frank-Wolfe algorithms”. Talk at Conference on Discrete Optimization and Machine Learning (Tokyo, Japan). [slides]
- WS/2023: Discrete Optimization and Machine Learning (seminar)
Recent Blog Posts.
- 01/2024: ZIB’s Anniversary: Celebrating 40 Years of Innovation in Mathematics and Computer Science
- 08/2023: Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond
- 08/2023: Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging
- 07/2023: How I Learned to Stop Worrying and Love Retraining
- 07/2023: Alternating Linear Minimization
News.
- 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]