Prior-Informed Generalizable Low-Rank Representation Learning via Regularized Adversarial Reweighting

Modern learning problems increasingly leverage data from heterogeneous sources to improve robustness and generalizability under distribution shift, often with high-dimensional parameters admitting low-rank structure. Existing group distributionally robust optimization (group-DRO) approaches optimize