4 citations · 9 across the 4 of their papers we have counts for
5 papers
PAC-Bayes Generalisation Bounds for Dynamical Systems Including Stable RNNs
Deividas Eringis, John Leth, Zheng-Hua Tan +2
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 cl…
PAC-Bayesian bounds for learning LTI-ss systems with input from empirical loss
Deividas Eringis, John Leth, Zheng-Hua Tan +2
In this paper we derive a Probably Approxilmately Correct(PAC)-Bayesian error bound for linear time-invariant (LTI) stochastic dynamical systems with inputs. Such bounds are widesp…
PAC-Bayesian-Like Error Bound for a Class of Linear Time-Invariant Stochastic State-Space Models
Deividas Eringis, John Leth, Zheng-Hua Tan +2
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 (st…
Explicit construction of the minimum error variance estimator for stochastic LTI state-space systems
Deividas Eringis, John Leth, Zheng-Hua Tan +2
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 t…
PAC-Bayesian theory for stochastic LTI systems
Deividas Eringis, John Leth, Zheng-Hua Tan +3
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…