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

2026-09-08 20:33:20
Heinke Hihn
arXiv_AI
       
Abstract

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%.

Abstract (translated)

URL

https://arxiv.org/abs/2609.09433

PDF

https://arxiv.org/pdf/2609.09433.pdf

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