Towards Robust Out-of-Distribution Generalization Bounds via Sharpness

arXiv:2403.06392v1 Announce Type: new
Abstract: Generalizing to out-of-distribution (OOD) data or unseen domain, termed OOD generalization, still lacks appropriate theoretical guarantees. Canonical OOD bounds focus on different distance measurements between source and target domains but fail to consider the optimization property of the learned model. As empirically shown in recent work, the sharpness of learned minima influences OOD generalization. To bridge this gap between optimization and OOD generalization, we study the effect of sharpness on how a model tolerates data …

abstract arxiv canonical cs.lg data distribution domain domains focus optimization property robust type via work