Multi-source learning stops guarding every worst-case mix: pulling adversarial weights back to a reference keeps low-rank representations stable across time
In multi-source learning the deployment population's source composition is unknown, and optimizing against the worst source mixture can be overly conservative. PILLAR regularizes adversarial source weights toward a user-specified reference distribution while learning a shared low-rank matrix, solving the resulting nonconvex-concave problem by bifactored gradient descent with mirror ascent, with linear convergence and an error bound that separates low-rank estimation from adversarial reweighting. Simulations and an electronic health record study of suicide-attempt risk show improved worst-source and transported predictive accuracy.