We study a worst-case scenario in generalization: Out-of-domain
generalization from a single source. The goal is to learn a robust model from a
single source and expect it to generalize over many unknown distributions. This
challenging problem has been seldom investigated while existing solutions
suffer from various limitations. In this paper, we propose a n