  We consider the setting of vector valued non-linear dynamical systems
$X_{t+1} = \phi(A^* X_t) + \eta_t$, where $\eta_t$ is unbiased noise and $\phi
: \mathbb{R} \to \mathbb{R}$ is a known link function that satisfies certain
{\em expansivity property}. The goal is to learn $A^*$ from a single trajectory
$X_1,\cdots,X_T$ of {\em dependent or correlated} samples. While the problem is
well-studied in the linear case, where $\phi$ is identity, with optimal error
rates even for non-mixing systems, existing results in the non-linear case hold
only for mixing systems. In this work, we improve existing results for learning
nonlinear systems in a number of ways: a) we provide the first offline
algorithm that can learn non-linear dynamical systems without the mixing
assumption, b) we significantly improve upon the sample complexity of existing
results for mixing systems, c) in the much harder one-pass, streaming setting
we study a SGD with Reverse Experience Replay ($\mathsf{SGD-RER}$) method, and
demonstrate that for mixing systems, it achieves the same sample complexity as
our offline algorithm, d) we justify the expansivity assumption by showing that
for the popular ReLU link function -- a non-expansive but easy to learn link
function with i.i.d. samples -- any method would require exponentially many
samples (with respect to dimension of $X_t$) from the dynamical system. We
validate our results via. simulations and demonstrate that a naive application
of SGD can be highly sub-optimal. Indeed, our work demonstrates that for
correlated data, specialized methods designed for the dependency structure in
data can significantly outperform standard SGD based methods.

Machine Learning;Optimization and Control;Machine Learning