EMOE: Expansive Matching of Experts for Robust Uncertainty Based Rejection

Yunni Qu, James Wellnitz, Alexander Tropsha, Junier Oliva

Keywords: out of distribution generalization, OOD, reject option, data augmentation

Abstract: 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 both image and tabular data.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 4995