A Short Information-Theoretic Analysis of Linear Auto-Regressive Learning
Abstract
In this note, we give a short information-theoretic proof of the consistency of the Gaussian maximum likelihood estimator in linear auto-regressive models. Our proof yields nearly optimal non-asymptotic rates for parameter recovery and works without any invocation of stability in the case of finite hypothesis classes.
Cite
Text
Ziemann. "A Short Information-Theoretic Analysis of Linear Auto-Regressive Learning." Proceedings of the 7th Annual Learning for Dynamics \& Control Conference, 2025.Markdown
[Ziemann. "A Short Information-Theoretic Analysis of Linear Auto-Regressive Learning." Proceedings of the 7th Annual Learning for Dynamics \& Control Conference, 2025.](https://mlanthology.org/l4dc/2025/ziemann2025l4dc-short/)BibTeX
@inproceedings{ziemann2025l4dc-short,
title = {{A Short Information-Theoretic Analysis of Linear Auto-Regressive Learning}},
author = {Ziemann, Ingvar},
booktitle = {Proceedings of the 7th Annual Learning for Dynamics \& Control Conference},
year = {2025},
pages = {26-30},
volume = {283},
url = {https://mlanthology.org/l4dc/2025/ziemann2025l4dc-short/}
}