Sebastian Pokutta
David M. McKenney Family Associate Professor
Associate Director of the Center for Machine Learning @ GT

Georgia Institute of Technology
Industrial and Systems Engineering (ISyE)
Algorithms and Randomness Center (ARC)
Center for Machine Learning @ GT (ML@GT)

Prospective Students/Postdocs. Please note that I unfortunately will be unable to respond to most inquiries regarding openings in my group. The admissions process at Georgia Tech is handled by the respective departments and not individual faculty. If you are already a student at GT or have been admitted, please feel free to contact me if you are interested in my research.

Recent Papers.

• Restarting Frank-Wolfe
  T. Kerdreux, A. d’Aspremont, S. Pokutta
  preprint (2018), [arXiv]
• Principled Deep Neural Network Training through Linear Programming
  D. Bienstock, G. Muñoz, S. Pokutta
  preprint (2018), [arXiv] [summary]
• An Online-Learning Approach to Inverse Optimization
  A. Bärmann, A. Martin, S. Pokutta, O. Schneider
  preprint (2018), [arXiv] [summary]
• Limits of Treewidth-based tractability in Optimization
  Y. Faenza, G. Muñoz, S. Pokutta
  preprint (2018), [arXiv] [summary]
• Blended Conditional Gradients: the unconditioning of conditional gradients
  G. Braun, S. Pokutta, D. Tu, S. Wright
  preprint (2018), [arXiv]

Select Recent Talks.

• 01/2019: (technical) “Smooth Constraint Convex Minimization via Conditional Gradients” [slides]. Plenary at INFORMS Computing Society Conference
• 11/2018: (technical) “Blended Conditional Gradients” at Oberwolfach [slides] (unfortunately the animations do not work; see [arXiv] for exact algorithm)
• 04/2018: (non-technical) “When the AI God fails: Recent Adventures in AI Hacking, Security, and Fairness” at the Hong Kong Analytics Community Meeting [slides]
• 04/2018: (technical) “Emulating the Expert: Inverse Optimization through Online Learning” at the “Optimization and Discrete Geometry” Workshop in Tel Aviv [slides] [arXiv]

News.

• 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.
• 12/2018: We are organizing the second workshop on “Discrete Optimization and Machine Learning” in July 2019 at the Center for Advanced Intelligence in Tokyo.
• 08/2018: We released our Blended Conditional Gradients code on github.
• Summer 2018: We are organizing a workshop on “Discrete Optimization and Machine Learning” in July 2018 at the Center for Advanced Intelligence in Tokyo.
• Spring 2018: We are organizing the “1st Transatlantic-Transpacific Workshop on Machine Learning and Discrete Optimization” in March 2018.
• Spring 2018: Teaching a special topics course on Machine Learning and Discrete Decisions and a VIP project (with M. Bloch) on Machine Learning in Wireless Communication.
• 05/2017: Georgia Tech has now a Ph.D. Program in Machine Learning.
• 12/2016: Passed the first hurdle of the DARPA SC2 challenge [project page].
• 06/2016: Official launch of Machine Learning @ GT Interdisciplinary Research Center.