# Contraction-Based Methods for Stable Identification and Robust Machine Learning: a Tutorial

arXiv: https://arxiv.org/abs/2110.00207 · HTML: https://mlsanity.com/paper/2110.00207/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2021.

## Built on by (most cited first, 3 of 3)

- [Learning Stable Koopman Embeddings](https://mlsanity.com/paper/2110.06509/) (2021)
- [Stable Linear Subspace Identification: A Machine Learning Approach](https://mlsanity.com/paper/2311.03197/) (2023)
- [On the improvement of model-predictive controllers](https://mlsanity.com/paper/2308.15157/) (2023)

## Abstract (arXiv)

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.