arXiv AI October 2, 2026 By Mehmet Baygin, Sengul Dogan, Turker Tuncer

Model validation in machine learning: A scenario-based guide from hold-out splits to nested group cross-validation in biomedical and applied research

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safety

arXiv Machine Learning

Sep 22

Leakage-Safe Empirical Benchmarking of EEG-Based Machine Learning Pipelines for Dementia Classification

arXiv:2609.22092v1 Announce Type: cross Abstract: Electroencephalography (EEG) is a low-cost and non-invasive signal source for dementia screening, yet existing EEG-based studies remain difficult to...

By Haitian Wang, Chamara Madarasingha, Redowan Mahmud, Aneesh Krishna, Ryu Takechi

computer-vision multimodal benchmarks

arXiv Machine Learning

Aug 18

On Cross-Validation for Hyperparameter Optimization of Deep Learning Image Classifiers

arXiv:2608. 14705v1 Announce Type: cross Abstract: Hyperparameter optimization (HPO) can materially affect the performance of deep learning (DL) image classifiers, but there is little empirical guidance on how to derive the validation signal that drives it, especially for the small sample sizes common in fields such as medical imaging.

By Ljubomir Buturovic (East Palo Alto, United States)

llms computer-vision

arXiv Machine Learning

Jun 2

RobustModelMaker: Coupling Bootstrap Stability Selection with Leakage-Safe Nested Cross-Validation for Scientific Machine Learning

arXiv:2606. 01566v1 Announce Type: new Abstract: Small-to-medium scientific datasets place machine learning pipelines under two compounding pressures.

By Amanda S Barnard

arXiv AI

Jul 28

Closed-Loop Validation-Repair for Healthcare Interoperability: A Multi-Model Study of Schema Compliance in Clinical LLMs

arXiv:2607. 24371v1 Announce Type: cross Abstract: Healthcare interoperability requires AI systems to produce structured outputs conforming to standardized schemas including ICD-10 for diagnostic coding, CPT for procedure billing, and HL7 FHIR for data exchange.

By Jianru Shen

llms

arXiv Machine Learning

4d ago

Best Practices in EEG Analysis: Preprocessing, Modeling, and Machine Learning

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By Parsa Razmara, Woojae Jeong, Aditya Kommineni, Raymundo Cassani, Richard Leahy, Takfarinas Medani

benchmarks

arXiv AI

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Personalized Scorer Modeling: A Learning-Based Framework for Deriving Robust Sleep Stage Labels from Multiple Experts

arXiv:2608. 12446v1 Announce Type: cross Abstract: Sleep stage classification is important for the diagnosis and management of sleep disorders, yet most automatic staging studies evaluate models against a single reference hypnogram despite known inter-scorer variability.

By Seyyed Ali Hoseini, Javad Baseri, Hamid Saadatfar, Edris Hoseini Gol, AmirHossein Eshghi

One email a morning, machine-written

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