gradient-based-methods

This paper demonstrates that two-layer neural networks trained with gradient-based methods can achieve the optimal computational-statistical tradeoff for learning Gaussian single-index models, matching the SQ lower bound up to polylogarithmic factors for all generative exponents and extending to sparse settings with a novel weight perturbation technique.