This paper advances the theory and practice of Domain Generalization (DG) in machine learning. We consider typical DG setting where hypothesis is composed a representation mapping followed by labeling function. Within this setting, majority popular methods aim to jointly learn functions minimizing well-known upper bound for classification risk unseen domain. In practice, however, based on theor...