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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.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-03331763
Contributor : Mihaly Petreczky Connect in order to contact the contributor
Submitted on : Thursday, September 2, 2021 - 10:06:55 AM
Last modification on : Monday, January 10, 2022 - 12:24:42 PM
Long-term archiving on: : Friday, December 3, 2021 - 7:38:31 PM

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  • HAL Id : hal-03331763, version 1
  • ARXIV : 2103.12866

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Deividas Eringis, John Leth, Zheng-Hua Tan, Rafal Wisniewski, Alireza Fakhrizadeh Esfahani, et al.. PAC-Bayesian theory for stochastic LTI systems. IEEE Conference on Decision and Control, Dec 2021, Austin, United States. ⟨hal-03331763⟩

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