Contraction-Based Methods for Stable Identification and Robust Machine Learning: a Tutorial
Systems and Control
2021-10-04 v1 Systems and Control
Optimization and Control
Abstract
This tutorial paper provides an introduction to recently developed tools for machine learning, especially learning dynamical systems (system identification), with stability and robustness constraints. The main ideas are drawn from contraction analysis and robust control, but adapted to problems in which large-scale models can be learnt with behavioural guarantees. We illustrate the methods with applications in robust image recognition and system identification.
Cite
@article{arxiv.2110.00207,
title = {Contraction-Based Methods for Stable Identification and Robust Machine Learning: a Tutorial},
author = {Ian R. Manchester and Max Revay and Ruigang Wang},
journal= {arXiv preprint arXiv:2110.00207},
year = {2021}
}
Comments
Paper in the invited tutorial session "Contraction Theory for Machine Learning" at 60th IEEE Conference on Decision and Control, 2021