Non-Parametric Learning of Stochastic Differential Equations with Fast Convergence Rates

A novel non-parametric learning paradigm is proposed for the identification of drift and diffusion coefficients of multi-dimensional non-linear stochastic differential equations, which relies on discrete-time observations of the state. The method provides theoretical estimates of non-asymptotic learning rates that become increasingly tighter as the regularity of the unknown coefficients increases.