  Neural Architecture Search remains a very challenging meta-learning problem.
Several recent techniques based on parameter-sharing idea have focused on
reducing the NAS running time by leveraging proxy models, leading to
architectures with competitive performance compared to those with hand-crafted
designs. In this paper, we propose an iterative technique for NAS, inspired by
algorithms for learning low-degree sparse Boolean functions. We validate our
approach on the DARTs search space (Liu et al., 2018b) and NAS-Bench-201 (Yang
et al., 2020). In addition, we provide theoretical analysis via upper bounds on
the number of validation error measurements needed for reliable learning, and
include ablation studies to further in-depth understanding of our technique.
