Connections Between Optimization in Machine Learning and Adaptive Control
Abstract
This paper explores many immediate connections between adaptive control and machine learning, both through common update laws as well as common concepts. Adaptive control as a field has focused on mathematical rigor and guaranteed convergence. The rapid advances in machine learning on the other hand have brought about a plethora of new techniques and problems for learning. This paper elucidates many of the numerous common connections between both fields such that results from both may be leveraged together to solve new problems. In particular, a specific problem related to higher order learning is solved through insights obtained from these intersections.
Cite
Text
Gaudio et al. "Connections Between Optimization in Machine Learning and Adaptive Control." ICML 2019 Workshops: AMTL, 2019.Markdown
[Gaudio et al. "Connections Between Optimization in Machine Learning and Adaptive Control." ICML 2019 Workshops: AMTL, 2019.](https://mlanthology.org/icmlw/2019/gaudio2019icmlw-connections/)BibTeX
@inproceedings{gaudio2019icmlw-connections,
title = {{Connections Between Optimization in Machine Learning and Adaptive Control}},
author = {Gaudio, Joseph E. and Gibson, Travis E. and Annaswamy, Anuradha M. and Bolender, Michael A. and Lavretsky, Eugene},
booktitle = {ICML 2019 Workshops: AMTL},
year = {2019},
url = {https://mlanthology.org/icmlw/2019/gaudio2019icmlw-connections/}
}