Prediction-powered Neural Architecture Search

2026-10-01

AI Summary

This study addresses the challenges of scarce ground-truth performance labels and noisy zero-cost proxies in neural architecture search (NAS) by proposing PPNAS. This method pioneers the integration of predictive performance inference (PPI) into label-efficient NAS, effectively synergizing multi-source heterogeneous signals by fusing a limited number of ground-truth labels with abundant zero-cost proxy data. Specifically, it leverages ordinal information to construct debiased pairwise ranking supervision. Experimental results demonstrate that PPNAS achieves state-of-the-art end-to-end NAS performance under constrained evaluation budgets, establishing a new paradigm for low-cost architecture search.

Abstract

Evaluating candidate architectures in neural architecture search (NAS) faces an inherent trade-off: on the one hand, reliable performance labels are limited because training and evaluating architectures is costly; on the other hand, zero-cost proxies (ZCPs) are cheap to compute at large scale but can be noisy. Yet, how to effectively combine these two sources of supervision remains unclear. In this paper, we propose PPNAS, a novel prediction-powered inference (PPI) approach for NAS. PPNAS fuses (1) a small set of architectures with observed performance labels and (2) a large set of architectures with ZCP information. To combine these two sources of supervision, PPNAS exploits the ordinal information provided by ZCPs to construct additional pairwise ranking supervision, while PPI debiases systematic discrepancies between ZCP-based and true performance rankings. We evaluate PPNAS in end-to-end predictor-based NAS, where it achieves state-of-the-art under limited evaluation budgets. To the best of our knowledge, PPNAS is the first prediction-powered approach for label-efficient NAS.

Problem

• Neural Architecture Search
• Zero-cost Proxies
• Label Efficiency
• Performance Prediction

Innovation

• Prediction-powered Inference
• Neural Architecture Search
• Zero-cost Proxies
• Pairwise Ranking
• Label-efficient NAS