1 code implementation • 15 Dec 2023 • Deividas Eringis, John Leth, Zheng-Hua Tan, Rafal Wisniewski, Mihaly Petreczky
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.
no code implementations • 29 Mar 2023 • Deividas Eringis, John Leth, Zheng-Hua Tan, Rafael Wisniewski, Mihaly Petreczky
In this paper we derive a Probably Approxilmately Correct(PAC)-Bayesian error bound for linear time-invariant (LTI) stochastic dynamical systems with inputs.
no code implementations • 30 Dec 2022 • Deividas Eringis, John Leth, Zheng-Hua Tan, Rafal Wisniewski, Mihaly Petreczky
In this paper we derive a PAC-Bayesian-Like 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).
no code implementations • 16 Jun 2022 • Martin Gonzalez, Hatem Hajri, Loic Cantat, Mihaly Petreczky
We investigate the problems and challenges of evaluating the robustness of Differential Equation-based (DE) networks against synthetic distribution shifts.
no code implementations • 24 May 2022 • Martin Gonzalez, Thibault Defourneau, Hatem Hajri, Mihaly Petreczky
In this paper we show that neural ODE analogs of recurrent (ODE-RNN) and Long Short-Term Memory (ODE-LSTM) networks can be algorithmically embeddeded into the class of polynomial systems.
no code implementations • 6 Sep 2021 • Deividas Eringis, John Leth, Zheng-Hua Tan, Rafal Wisniewski, Mihaly Petreczky
In this short article, we showcase the derivation of the optimal (minimum error variance) estimator, when one part of the stochastic LTI system output is not measured but is able to be predicted from the measured system outputs.
no code implementations • 21 Apr 2021 • Ion Victor Gosea, Mihaly Petreczky, Athanasios C. Antoulas
We propose a model reduction method for LPV systems.
no code implementations • 23 Mar 2021 • Deividas Eringis, John Leth, Zheng-Hua Tan, Rafal Wisniewski, Alireza Fakhrizadeh Esfahani, Mihaly Petreczky
In this paper we derive a PAC-Bayesian error bound for autonomous stochastic LTI state-space models.
no code implementations • 6 Dec 2019 • Vera Shalaeva, Alireza Fakhrizadeh Esfahani, Pascal Germain, Mihaly Petreczky
In this paper, we improve the PAC-Bayesian error bound for linear regression derived in Germain et al. [10].