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Found 9 papers, 1 papers with code
PAC-Bayes Generalisation Bounds for Dynamical Systems Including Stable RNNs
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.
PAC-Bayesian bounds for learning LTI-ss systems with input from empirical loss
In this paper we derive a Probably Approxilmately Correct(PAC)-Bayesian error bound for linear time-invariant (LTI) stochastic dynamical systems with inputs.
PAC-Bayesian-Like Error Bound for a Class of Linear Time-Invariant Stochastic State-Space Models
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).
Noisy Learning for Neural ODEs Acts as a Robustness Locus Widening
We investigate the problems and challenges of evaluating the robustness of Differential Equation-based (DE) networks against synthetic distribution shifts.
Realization Theory Of Recurrent Neural ODEs Using Polynomial System Embeddings
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.
Explicit construction of the minimum error variance estimator for stochastic LTI state-space systems
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.
Reduced-order modeling of LPV systems in the Loewner framework
We propose a model reduction method for LPV systems.
PAC-Bayesian theory for stochastic LTI systems
In this paper we derive a PAC-Bayesian error bound for autonomous stochastic LTI state-space models.
Improved PAC-Bayesian Bounds for Linear Regression
In this paper, we improve the PAC-Bayesian error bound for linear regression derived in Germain et al. [10].
