arXiv Machine Learning August 14, 2026 By Kacper Jurek, Wojciech Batko, Marek \'Smieja, Marcin Przewi\k{e}\'zlikowski

SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data

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arXiv:2605. 08519v2 Announce Type: replace Abstract: Learning from scarce labeled data with a larger pool of unlabeled samples, known as semi-supervised few-shot learning (SS-FSL), remains critical for applications involving tabular data in domains like medicine, finance, and science.

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