Label Noise Robustness for Domain-Agnostic Fair Corrections via Nearest Neighbors Label Spreading
<|reference_start|>Label Noise Robustness for Domain-Agnostic Fair Corrections via Nearest Neighbors Label Spreading: Last-layer retraining methods have emerged as an efficient framework for correcting existing base models. Within this framework, several methods have been proposed to deal with correcting models for subgroup fairness with and without group membership information. Importantly, prior work has demonstrated that many methods are susceptible to noisy labels. To this end, we propose a drop-in correction for label noise in last-layer retraining, and demonstrate that it achieves state-of-the-art worst-group accuracy for a broad range of symmetric label noise and across a wide variety of datasets exhibiting spurious correlations. Our proposed approach uses label spreading on a latent nearest neighbors graph and has minimal computational overhead compared to existing methods.<|reference_end|>
@article{stromberg2024label,
    title={Label Noise Robustness for Domain-Agnostic Fair Corrections via Nearest
  Neighbors Label Spreading},
    author={Nathan Stromberg and Rohan Ayyagari and Sanmi Koyejo and Richard Nock
  and Lalitha Sankar},
    journal={arXiv preprint arXiv:2406.09561},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09561},
    primaryClass={cs.LG cs.AI}
}