  In this work we consider the problem of repeated hyperparameter and neural
architecture search (HNAS). We propose an extension of Successive Halving that
is able to leverage information gained in previous HNAS problems with the goal
of saving computational resources. We empirically demonstrate that our solution
is able to drastically decrease costs while maintaining accuracy and being
robust to negative transfer. Our method is significantly simpler than competing
transfer learning approaches, setting a new baseline for transfer learning in
HNAS.
