Out-of-distribution (OOD) generalization is a challenging machine learning problem yet highly desirable in many high-stake applications. Distributionally robust optimization (DRO) is a promising learning paradigm to tackle this challenge but suffers from several limitations. To address this challenge, we propose graph-relational distributionally robust optimization that trains OOD-resilient machine learning models by exploiting the topological structure of data distributions. Our approach can uniformly handle both fully-known and partially-known topological structures. Empirical results on both synthetic and real-world datasets demonstrate the effectiveness and flexibility of our method.
Fengchun Qiao (University of Delaware)
Xi Peng (University of Delaware)
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