  A majority of recent developments in neural architecture search (NAS) have
been aimed at decreasing the computational cost of various techniques without
affecting their final performance. Towards this goal, several low-fidelity and
performance prediction methods have been considered, including those that train
only on subsets of the training data. In this work, we present an adaptive
subset selection approach to NAS and present it as complementary to
state-of-the-art NAS approaches. We uncover a natural connection between
one-shot NAS algorithms and adaptive subset selection and devise an algorithm
that makes use of state-of-the-art techniques from both areas. We use these
techniques to substantially reduce the runtime of DARTS-PT (a leading one-shot
NAS algorithm), as well as BOHB and DEHB (leading multifidelity optimization
algorithms), without sacrificing accuracy. Our results are consistent across
multiple datasets, and towards full reproducibility, we release our code at
https: //anonymous.4open.science/r/SubsetSelection NAS-B132.
