arXiv Machine Learning June 8, 2026 By Zhuphua Cao

A machine-learning-assisted progressive digit-randomness screening framework for detecting non-random patterns in raw numerical research data

Read the original on arXiv Machine Learning →

arXiv:2606. 07128v1 Announce Type: new Abstract: Raw numerical datasets remain less systematically examined in integrity screening than images, plagiarism, or summary-statistic inconsistencies.

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When Single-Dataset Conclusions Fail: A 45-Task Study of Threshold Tuning and Resampling for Imbalanced Classification

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arXiv Machine Learning

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benchmarks safety

arXiv Machine Learning

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When Single-Dataset Conclusions Fail: A 45-Task Study of Threshold Tuning and Resampling for Imbalanced Classification

Hard Cases, Bad Labels: Testing Error Exposure and Error Location in Uncertainty Sampling Under Bounded Label Noise

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Synthetic minority data is redundant or invalid: a data-dependent validity theory and a de-biased test