# Efficient Leakage-Free Neural Architecture Search under Leave-One-Subject-Out Evaluation

arXiv: https://arxiv.org/abs/2609.09433 · HTML: https://mlsanity.com/paper/2609.09433/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 8 Sep 2026.

## Builds on (most cited first, 4 of 4)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions](https://mlsanity.com/paper/2006.02903/) (2020)
- [Harmonic-NAS: Hardware-Aware Multimodal Neural Architecture Search on Resource-constrained Devices](https://mlsanity.com/paper/2309.06612/) (2023)
- [Cross Task Neural Architecture Search for EEG Signal Classifications](https://mlsanity.com/paper/2210.06298/) (2022)

## Abstract (arXiv)

Leave-One-Subject-Out (LOSO) evaluation estimates generalisation performance for subject-based classification but makes Neural Architecture Search (NAS) computationally expensive because a fully nested implementation requires N independent architecture searches and, assuming approximately linear training cost, scales as O(N^2). We propose a leakage-free, block-based approach that shares NAS runs across subjects. On the BioVid Heat Pain dataset, our approach increased the mean accuracy from 82.79% to 83.39% while reducing the number of parameters by up to 99.2%.