EMOE: Expansive Matching of Experts for Robust Uncertainty Based Rejection
<|reference_start|>EMOE: Expansive Matching of Experts for Robust Uncertainty Based Rejection: 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.<|reference_end|>
@article{qu2024emoe:,
    title={EMOE: Expansive Matching of Experts for Robust Uncertainty Based
  Rejection},
    author={Yunni Qu (1), James Wellnitz (2), Alexander Tropsha (2), Junier Oliva
  (1) ((1) Department of Computer Science, University of North Carolina at
  Chapel Hill, (2) Eshelman School of Pharmacy, University of North Carolina at
  Chapel Hill)},
    journal={arXiv preprint arXiv:2406.01825},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01825},
    primaryClass={cs.LG cs.AI}
}