Unification and Optimization of Robust Supervised Learning
The authors created a unified framework for robust learning by organizing robustness methods along three design axes: reference distribution, ambiguity set, and disambiguation principle. This allows researchers to jointly optimize all robustness strategies through a single training procedure. The framework shows that combining these choices often outperforms using any single method.