  Expansive Matching of Experts (EMOE) is a novel method that utilizes
support-expanding, extrapolatory pseudo-labeling to improve prediction and
uncertainty based rejection on out-of-distribution (OOD) points. We propose an
expansive data augmentation technique that generates OOD instances in a latent
space, and an empirical trial based approach to filter out augmented expansive
points for pseudo-labeling. EMOE utilizes a diverse set of multiple base
experts as pseudo-labelers on the augmented data to improve OOD performance
through a shared MLP with multiple heads (one per expert). We demonstrate that
EMOE achieves superior performance compared to state-of-the-art methods on
tabular data.
