@inproceedings{
stromberg2024enhancing,
title={Enhancing Robustness of Last Layer Two-Stage Fair Model Corrections},
author={Nathan Stromberg and Rohan Ayyagari and Sanmi Koyejo and Richard Nock and Lalitha Sankar},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ChnJ3W4HFG}
}
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.
Enhancing Robustness of Last Layer Two-Stage Fair Model Corrections
Nathan Stromberg Rohan Ayyagari Sanmi Koyejo Richard Nock Lalitha Sankar