Skip to yearly menu bar Skip to main content


Poster
in
Workshop: Workshop on Distribution Shifts: Connecting Methods and Applications

Graph-Relational Distributionally Robust Optimization

Fengchun Qiao · Xi Peng


Abstract:

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.

Chat is not available.