Efficient Leakage-Free Neural Architecture Search under Leave-One-Subject-Out Evaluation
cs.LG, cs.AI
Submitted: 2026-09-08
Updated: 2026-09-08
License: http://creativecommons.org/licenses/by/4.0/
The gist: Leave-One-Subject-Out (LOSO) evaluation estimates generalisation performance for subject-based classification but makes Neural Architecture Search (NAS) computationally expensive because a fully
Terminology
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%.
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