We analyze imbalanced training loss, showing that gradient descent dynamics can gradually reduce bias and recover minority-specific features with longer training.

We propose a constraint-aware decision-focused learning framework that enables parameter prediction in constraints, without making any assumptions about the underlying optimization problem.