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.
- Locally Accelerated Conditional Gradients
A. Carderera, J. Diakonikolas, S. Pokutta
preprint (2019), [arXiv] [summary] [slides]
- Blended Matching Pursuit
C. Combettes, S. Pokutta
to appear in Proceedings of NeurIPS (2019), [arXiv] [summary] [slides]
- Restarting Frank-Wolfe
T. Kerdreux, A. d’Aspremont, S. Pokutta
to appear in Proceedings of AISTATS (2019), [arXiv] [summary] [slides]
- Principled Deep Neural Network Training through Linear Programming
D. Bienstock, G. Muñoz, S. Pokutta
preprint (2018), [arXiv] [summary]
- Blended Conditional Gradients: the unconditioning of conditional gradients
G. Braun, S. Pokutta, D. Tu, S. Wright
to appear in Proceedings of ICML (2019), [arXiv] [summary] [poster] [slides]
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.
- 08/2019: Universal Portfolios: how to (not) get rich
- 08/2019: Toolchain Tuesday No. 6
- 07/2019: Conditional Gradients and Acceleration
- 06/2019: Cheat Sheet: Acceleration from First Principles
- 05/2019: Blended Matching Pursuit
- 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.