Prof. Dr. Sebastian Pokutta

Vice President
Zuse Institute Berlin (ZIB)

Professor
Optimization and Machine Learning
Institute of Mathematics
Electrical Engineering and Computer Science (courtesy)
Technische Universität Berlin

Research Lab. My lab 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.

Recent Papers.

1. Carderera, A., and Pokutta, S. (2020). Second-order Conditional Gradients. Preprint. [arXiv] [code]
2. Pfetsch, M., and Pokutta, S. (2020). IPBoost – Non-Convex Boosting via Integer Programming. Preprint. [arXiv] [summary]
3. Pokutta, S., Singh, M., and Torrico, A. (2020). On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness. Preprint. [arXiv] [poster]
4. Diakonikolas, J., Carderera, A., and Pokutta, S. (2020). Locally Accelerated Conditional Gradients. To Appear in Proceedings of AISTATS. [arXiv] [summary] [slides] [code]
5. Combettes, C. W., and Pokutta, S. (2019). Revisiting the Approximate Carathéodory Problem via the Frank-Wolfe Algorithm. Preprint. [arXiv] [slides] [code]

Select Recent Talks and Teaching.

• 09/2019: (technical) “Smooth Constraint Convex Minimization via Conditional Gradients”. Plenary at the 19th French-German-Swiss conference on Optimization. [slides]
• 09/2019: (technical) “Mirror Descent and related methods in Linear and Discrete Optimization” at Cargese Workshop on Combinatorial Optimization. [slides]
• 07/2019: (technical) “Locally Accelerated Conditional Gradients” at RIMS, Kyoto. [slides]
• 01/2019: (technical) “Smooth Constraint Convex Minimization via Conditional Gradients”. Plenary at INFORMS Computing Society Conference. [slides]
• 11/2018: (technical) “Blended Conditional Gradients” at Oberwolfach (unfortunately the animations do not work; see [arXiv] for exact algorithm). [slides]
• WS/2019: Discrete Optimization and Machine Learning (Seminar).

Recent Blog Posts.

• 02/2020: Non-Convex Boosting via Integer Programming
• 11/2019: Approximate Carathéodory via Frank-Wolfe
• 09/2019: SCIP x Raspberry Pi: SCIP on Edge
• 08/2019: Universal Portfolios: how to (not) get rich
• 08/2019: Toolchain Tuesday No. 6

News.

• 09/2019: We are organizing the third workshop on “Discrete Optimization and Machine Learning” in May 2020 at Kyoto University in Kyoto.
• 09/2019: TU Berlin and the Berlin School of Mathematics, with the support of MATH+, are organizing the “Combinatorial Optimization at Work (Co@Work) 2020” summer school on September 14 - 26, 2020 at ZIB in Berlin. Application deadline is: June 14, 2020. Intended audience: master/PhD students, Post-docs.
• 09/2019: Sanjeena Dang, Antoine Deza, Swati Gupta, Paul McNicholas, Masashi Sugiyama, and I are organizing a focus program on Data Science and Optimization in November 2019 at the Fields Institute in Toronto.
• 12/2018: Antoine Deza, Takanori Maehara, and I are editing a special issue on “Machine Learning and Discrete Optimization” in Discrete Optimization. Deadline for submission is May, 30th 2019.
• 04/2019: Shipra Agrawal, Adam Elmachtoub, and I are organizing a track on Industrial Engineering and Operations Research at “Machine Learning in Science and Engineering (MLSE)” in June 2019 at the Georgia Institute of Technology in Atlanta.