# One-Shot Neural Architecture Search via Compressive Sensing

arXiv: https://arxiv.org/abs/1906.02869 · HTML: https://mlsanity.com/paper/1906.02869/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2019.

## Built on by (most cited first, 4 of 4)

- [Sphynx: ReLU-Efficient Network Design for Private Inference](https://mlsanity.com/paper/2106.11755/) (2021)
- [Fisher Task Distance and Its Application in Neural Architecture Search](https://mlsanity.com/paper/2103.12827/) (2021)
- [Improved Automated Machine Learning from Transfer Learning](https://mlsanity.com/paper/2103.00241/) (2021)
- [Discovering Sparse Recovery Algorithms Using Neural Architecture Search](https://mlsanity.com/paper/2512.21563/) (2025)

## Abstract (arXiv)

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.