Events Detail

Speaker:
Tobias Sutter

This event is part of an event series „Logic Colloquium“.

Abstract: Given the recent progress in information technology with real-time data
being available at large scale, many complex tasks involving dynamical environments
are addressed via tools from machine learning, control theory and optimization.
While control theory in the past has mainly focused on model based design the
advent of large scale data sets raises the possibility to analyse dynamical systems
on the basis of data rather than analytical models. From a machine learning per-
spective, one of the main challenges going forward is to tackle problems involving
dynamical systems which are beyond static pattern recognition problems. In this
talk, I will give an overview about different problems lying in this intersection of
dynamical systems, learning and control that I have worked on in the past. In par-
ticular, I will discuss how to efficiently learn a linear dynamical system with stability
guarantees and how to identify its topological equivalence class based on a single
trajectory of correlated data.