📝 SummarXiv

Abstract

Expansive Matching of Experts (EMOE) is a novel framework that utilizes support-expanding, extrapolatory pseudo-labeling to improve prediction and uncertainty based rejection on out-of-distribution(OOD) points. EMOE utilizes a diverse set of multiple base experts as pseudo-labelers on the augmented data to improve OOD performance through multiple MLP heads (one per expert) with shared embedding train with a novel per-head matching loss. Unlike prior methods that rely on modality-specific augmentations or assume access to OOD data, EMOE introduces extrapolatory pseudo-labeling on latent-space augmentations, enabling robust OOD generalization with any real-valued vector data. In contrast to prior modality agnostic methods with neural backbones, EMOE is model-agnostic, working effectively with methods from simple tree-based models to complex OOD generalization models. We demonstrate that EMOE achieves superior performance compared to state-of-the-art method on diverse datasets in single-source domain generalization setting.