PAC-Bayesian theory for stochastic LTI systems

Deividas Eringis, John-Josef Leth, Zheng-Hua Tan, Rafal Wisniewski, Alireza Fakhrizadeh Esfahani, Mihaly Petreczky

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

1 Citation (Scopus)

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.
Original languageEnglish
Title of host publication2021 60th IEEE Conference on Decision and Control (CDC)
Number of pages8
PublisherIEEE
Publication date2021
Pages6626-6633
Article number9682808
ISBN (Print)978-1-6654-3660-1
ISBN (Electronic)978-1-6654-3659-5
DOIs
Publication statusPublished - 2021
Event2021 60th IEEE Conference on Decision and Control (CDC) - Austin, United States
Duration: 14 Dec 202117 Dec 2021

Conference

Conference2021 60th IEEE Conference on Decision and Control (CDC)
Country/TerritoryUnited States
CityAustin
Period14/12/202117/12/2021
SeriesI E E E Conference on Decision and Control. Proceedings
ISSN0743-1546

Fingerprint

Dive into the research topics of 'PAC-Bayesian theory for stochastic LTI systems'. Together they form a unique fingerprint.

Cite this