Deividas Eringis — accepted AI-conference papers

2024

PAC-Bayes Generalisation Bounds for Dynamical Systems including Stable RNNs

Deividas Eringis, John Leth, Zheng-Hua Tan, Rafael Wisniewski, Mihály Petreczky

AAAI 2024technical

In this paper, we derive a PAC-Bayes bound on the generalisation gap, in a supervised time-series setting for a special class of discrete-time non-linear dynamical systems. This class includes stable recurrent neural networks (RNN), and the motivation for this work was its application to RNNs. In or…

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2024

PAC-Bayesian Error Bound, via Rényi Divergence, for a Class of Linear Time-Invariant State-Space Models

Deividas Eringis, john leth, Zheng-Hua Tan, Rafal Wisniewski, Mihaly Petreczky

ICML 2024poster

In this paper we derive a PAC-Bayesian error bound for a class of stochastic dynamical systems with inputs, namely, for linear time-invariant stochastic state-space models (stochastic LTI systems for short). This class of systems is widely used in control engineering and econometrics, in particular,…

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