Although the optimization objectives for learning neural networks are highly nonconvex, gradient-based methods have been wildly successful at learning neural networks in practice. This juxtaposition has led to a number of recent studies on provable guarantees for neural networks trained by gradient descent. Unfortunately, the techniques in these works are often highly specific to the problem studied in each setting, relying on different assumptions on the distribution, optimization parameters, a…