We have these for many models and parts of models. On one side, you have sample complexity [0] (how much data do you need?). On the other side, in deep learning

We have these for many models and parts of models. On one side, you have sample complexity [0] (how much data do you need?). On the other side, in deep learning you also have optimization convergence rates (how many steps do you need find the right parameters?). For example, newton's method converges at a quadratic rate for many problems. I know people work on similar theories for non-convex optimization, but the actual work there is outside what I know. It would be great to have a breakthrough there. [0] https://en.wikipedia.org/wiki/Sample_complexity https://en.wikipedia.org/wiki/Sample_complexity