Benchmarking vision-based driving policies is challenging. On one hand, open-loop evaluation with real data is easy, but these results do not reflect closed-loop performance.

Despite the progress achieved with NAS, so far there is little attention to theoretical guarantees on NAS.

We consider linear prediction with a convex Lipschitz loss, or more generally, stochastic convex optimization problems of generalized linear form, i.e.

To this end, we follow Ainsworth et al.

Toourknowledge, ADASPIDER isthefirstparameterfree non-convex variance-reduction method in the sense that it does not require the knowledge of problem-dependent parameters, such as smoothness constant L,targetaccuracyϵoranybound ongradient norms.