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.
|Konference||2021 60th IEEE Conference on Decision and Control (CDC)|
|Periode||14/12/2021 → 17/12/2021|
|Navn||I E E E Conference on Decision and Control. Proceedings|