OverNaN extends common synthetic oversampling methods to operate directly on incomplete data vectors, preserving meaningful missingness structure during generation of minority-class samples.
Advances in Neural Information Processing Systems , volume =
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Benchmarking in pediatric ICU antimicrobial stewardship shows performance depends mainly on target prevalence and dataset traits rather than model complexity, with sequence models improving precision-recall at 24-hour resolution but showing poorer calibration than tabular models.
citing papers explorer
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OverNaN: NaN-Aware Oversampling for Imbalanced Learning with Meaningful Missingness
OverNaN extends common synthetic oversampling methods to operate directly on incomplete data vectors, preserving meaningful missingness structure during generation of minority-class samples.
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Benchmarking Machine Learning Architectures for Antimicrobial Stewardship in Pediatric ICUs
Benchmarking in pediatric ICU antimicrobial stewardship shows performance depends mainly on target prevalence and dataset traits rather than model complexity, with sequence models improving precision-recall at 24-hour resolution but showing poorer calibration than tabular models.