“Conditional Gradients in Machine Learning”
Speaker: Sebastian Pokutta (TU Berlin)
Monday, April 03, 2023, 4:00PM to 5:00PM
3:30pm – Pre-talk meet and greet teatime – Dana House, 24 Hillhouse Avenue
Location: Mason Lab, Rm. 211, 9 Hillhouse Avenue New Haven, CT 06511 or via Panopto
Abstract: Conditional Gradient methods are an important class of methods to minimize (non-)smooth convex functions over (combinatorial) polytopes. Recently these methods received a lot of attention as they allow for structured optimization and hence learning, incorporating the underlying polyhedral structure into solutions. In this talk I will give a broad overview of these methods, their applications, as well as present some recent results both in traditional optimization and learning as well as in deep learning.
Speaker Bio: Sebastian Pokutta is the Vice President of the Zuse Institute Berlin (ZIB) and a Professor of Mathematics at TU Berlin with a research focus on Artificial Intelligence and Optimization. Having received both his diploma and Ph.D. in mathematics from the University of Duisburg-Essen in Germany, Pokutta was a postdoctoral researcher and visiting lecturer at MIT, worked for IBM ILOG, and Krall Demmel Baumgarten. Prior to joining ZIB and TU Berlin, he was the David M. McKenney Family Associate Professor in the School of Industrial and Systems Engineering and an Associate Director of the Machine Learning @ GT Center at the Georgia Institute of Technology as well as a Professor at the University of Erlangen-Nürnberg. Sebastian received the David M. McKenney Family Early Career Professorship in 2016, an NSF CAREER Award in 2015, the Coca-Cola Early Career Professorship in 2014, the outstanding thesis award of the University of Duisburg-Essen in 2006, as well as various Best Paper awards.
Pokutta’s research is situated at the intersection of Artificial Intelligence and Optimization, combining Machine Learning with Discrete Optimization techniques as well as the Theory of Extended Formulations, exploring the limits of computation in alternative models of complexity. A particular focus are so-called Frank-Wolfe methods and conditional gradient methods due to their versatility in the context of constrained optimization and structured learning. Pokutta has also worked on applications of Optimization and Machine Learning, leveraging data in the context of pressing industrial and financial challenges. These areas include Supply Chain Management, Manufacturing, Cyber-Physical Systems (incl. Industrial Internet, Industry 4.0, Internet of Things), and Finance. Examples of Pokutta’s applied work include stowage optimization problems for inland vessels, oil production problems, clearing of electricity markets, order fulfillment problems, warehouse location problems, simulation of autonomous vehicle fleets, portfolio optimization problems, optimal liquidity management strategies, and predictive pregnancy diagnostics.
3:30pm – Pre-talk meet and greet teatime – Dana House, 24 Hillhouse Avenue
