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PAC-Bayesian theory for stochastic LTI systems
- Source :
- IEEE Conference on Decision and Control, IEEE Conference on Decision and Control, Dec 2021, Austin, United States, Eringis, D, Leth, J-J, Tan, Z-H, Wisniewski, R, Fakhrizadeh Esfahani, A & Petreczky, M 2021, PAC-Bayesian theory for stochastic LTI systems . in 2021 60th IEEE Conference on Decision and Control (CDC) ., 9682808, IEEE, I E E E Conference on Decision and Control. Proceedings, pp. 6626-6633, 2021 60th IEEE Conference on Decision and Control (CDC), Austin, Texas, United States, 14/12/2021 . https://doi.org/10.1109/CDC45484.2021.9682808
- Publication Year :
- 2021
- Publisher :
- HAL CCSD, 2021.
-
Abstract
- International audience; 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.
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- IEEE Conference on Decision and Control, IEEE Conference on Decision and Control, Dec 2021, Austin, United States, Eringis, D, Leth, J-J, Tan, Z-H, Wisniewski, R, Fakhrizadeh Esfahani, A & Petreczky, M 2021, PAC-Bayesian theory for stochastic LTI systems . in 2021 60th IEEE Conference on Decision and Control (CDC) ., 9682808, IEEE, I E E E Conference on Decision and Control. Proceedings, pp. 6626-6633, 2021 60th IEEE Conference on Decision and Control (CDC), Austin, Texas, United States, 14/12/2021 . https://doi.org/10.1109/CDC45484.2021.9682808
- Accession number :
- edsair.doi.dedup.....75861602f74d11baed473ac804d17538