Rethinking Data Augmentation under Covariate Shift: Invariant-Guided Diffusion and Prototype Reweighting.

arXiv:2610.00873v1 Announce Type: cross Abstract: In many industrial applications, 1) tabular data is scarce and imbalanced and thus requires synthetic expansion; 2) input distributions drift between training and deployment (covariate shift); 3) validation set