Training neural networks is a challenging non-convex optimization problem. Stein’s method provides a novel way to change optimization problem to a tensor decomposition problem for guaranteed training of two-layer neural networks. We provide risk bounds for our proposed method, with a polynomial sample complexity in the relevant parameters, such as input dimension and number of neurons. Our training method is based on tensor decomposition, which provably converges to the global optimum, under a set of mild non-degeneracy conditions. This provides insights into role of generative process for tractability of supervised learning.