AI RESEARCH
CTRL Your Shift: Clustered Transfer Residual Learning for Many Small Datasets
arXiv CS.LG
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ArXi:2508.11144v2 Announce Type: replace Machine learning (ML) tasks often utilize large-scale data that is drawn from several distinct sources, such as different locations, treatment arms, or groups. In such settings, practitioners often desire predictions that not only exhibit good overall accuracy, but also remain reliable within each source and preserve the differences that matter across sources.