A resource-efficient method for repeated HPO and NAS problems

Giovanni Zappella, David Salinas, Cédric Archambeau

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.

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