Speaker |
Kostas Margellos |
|---|---|
Affiliation |
University of Oxford |
Date |
Friday, 03 May 2024 |
Time |
12:00-13:00 |
Location |
Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH |
Link |
https://ucl.zoom.us/j/97245943682 |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
In this talk we consider convex optimization problems affected by uncertainty, where uncertainty is represented by means of samples/scenarios. We show how finite sample complexity bounds for the generalization properties of the resulting solutions can be obtained, using tools from statistical learning theory based on probably approximately correct learning. Specifically, we view this problem under a compression learning lens that allows for sharper bounds compared to Vapnik-Chervonenkis results. We next discuss how to trade (probabilistic) feasibility to optimality by introducing a sample discarding procedure. Existing results in this direction are not tight, often leading to a conservative behaviour as far as performance is concerned. We show how to overcome this and achieve a tight quantification of the feasibility-performance trade-off using a sequential methodology for sample discarding. Moreover, we discuss certain aspects of applying such methodology in a multi-agent setting, with each agent having access to a private set of samples. |
Biography |
Kostas Margellos received the Diploma degree in electrical engineering from the University of Patras, Patras, Greece, in 2008, and the Ph.D. degree in control engineering from ETH Zürich, Zürich, Switzerland, in 2012. He spent 2013–2015 as a Postdoctoral Researcher with ETH Zürich; UC Berkeley, Berkeley, CA, USA; and Politecnico di Milano, Milan, Italy, respectively. In 2016, he joined the Control Group, Department of Engineering Science, University of Oxford, Oxford, U.K., where he is currently an Associate Professor. He is also a Fellow of Reuben College, Oxford, U.K., and a Lecturer with Worcester College, Oxford, U.K. His research interests include optimization and control of complex uncertain systems, with applications to energy and transportation networks. He is an Associate Editor for Automatica and IEEE Control Systems Letters, and has been general co-chair of L4DC 2024. |