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
- Pokutta, S. (2021). Mathematik, Machine Learning und Artificial Intelligence. To Appear in Mitteilungen Der DMV (German). [PDF]
- Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2021). Projection-Free Optimization on Uniformly Convex Sets. To Appear in Proceedings of AISTATS. [arXiv] [summary]
- Braun, G., and Pokutta, S. (2021). Dual Prices for Frank-Wolfe Algorithms. Preprint. [arXiv]
- Carderera, A., Pokutta, S., Schütte, C., and Weiser, M. (2021). CINDy: Conditional gradient-based Identification of Non-linear Dynamics – Noise-robust recovery. Preprint. [arXiv] [summary]
- Combettes, C. W., and Pokutta, S. (2021). Complexity of Linear Minimization and Projection on Some Sets. Preprint. [arXiv] [code]
- Pokutta, S., Spiegel, C., and Zimmer, M. (2020). Deep Neural Network Training with Frank-Wolfe. Preprint. [arXiv] [summary] [code]
- Combettes, C. W., Spiegel, C., and Pokutta, S. (2020). Projection-Free Adaptive Gradients for Large-Scale Optimization. Preprint. [arXiv] [summary] [code]
- Carderera, A., and Pokutta, S. (2020). Second-order Conditional Gradient Sliding. Preprint. [arXiv] [summary] [code]
Select Recent Talks and Teaching.
- 11/2020: (technical) “Conditional Gradients: Overview and Recent Advances”. Talk at RWTH Aachen Mathematical Colloquium (online). [slides]
- 11/2020: (general) “AI and-for-with-against Humanity?”. Keynote at Human-centric Artificial Intelligence: 2nd French-German-Japanese Symposium (online). [slides]
- 09/2020: (technical) “Restarting Algorithms: Sometimes there is Free Lunch”. Keynote at CPAIOR 2020 (online). [slides] [video]
- 09/2020: (technical) “Robust ML Training with Conditional Gradients”. Talk at CO@Work 2020 Summer School (online). [slides] [video]
- 05/2020: (technical) “Beyond Worst-case Rates: Data-dependent Rates in Learning and Optimization”. Keynote at MIP 2020 (online). [slides] [video]
- WS/2020: Discrete Optimization and Machine Learning (seminar)
Recent Blog Posts.
- 01/2021: CINDy: Conditional gradient-based Identification of Non-linear Dynamics
- 11/2020: DNN Training with Frank–Wolfe
- 10/2020: Projection-Free Adaptive Gradients for Large-Scale Optimization
- 09/2020: Accelerating Domain Propagation via GPUs
- 08/2020: Join CO@Work and EWG-POR – online and for free!
- 11/2020: Our group received a Google Research Award to support our work on Integer Programming solvers.
- 10/2020: Four projects funded by the Math+ Research Center.
- 10/2020: HTW Berlin enters cooperation with Zuse Institute Berlin. ZIB Press Release, HTW Press Release (German)
- 05/2020: Our Special Priority Program (SPP) proposal ‘Theoretical Foundations of Deep Learning’ was funded by DFG. With an overall budget of EUR 8.5m, this program sets out to significantly boost our fundamental understanding of Deep Learning. The coordination team is Gitta Kutyniok (speaker), Martin Burger, Matthias Hein, Sebastian Pokutta, and Ingo Steinwart. DFG Press Release (German), TUB Press Release (German) SPP Homepage
- 04/2020: Research Campus MODAL enters second funding phase