@inproceedings{41f14ea790bf4ab0a2e153e453036d2b,
title = "PAC-Bayesian theory for stochastic LTI systems",
abstract = "In this paper we derive a PAC-Bayesian error bound for autonomous stochastic LTI state-space models. The motivation for deriving such error bounds is that they will allow deriving similar error bounds for more general dynamical systems, including recurrent neural networks. In turn, PAC-Bayesian error bounds are known to be useful for analyzing machine learning algorithms and for deriving new ones.",
author = "Deividas Eringis and John-Josef Leth and Zheng-Hua Tan and Rafal Wisniewski and {Fakhrizadeh Esfahani}, Alireza and Mihaly Petreczky",
year = "2021",
doi = "10.1109/CDC45484.2021.9682808",
language = "English",
isbn = "978-1-6654-3660-1",
series = "I E E E Conference on Decision and Control. Proceedings",
publisher = "IEEE",
pages = "6626--6633",
booktitle = "2021 60th IEEE Conference on Decision and Control (CDC)",
address = "United States",
note = "2021 60th IEEE Conference on Decision and Control (CDC) ; Conference date: 14-12-2021 Through 17-12-2021",
}