To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Models
<|reference_start|>To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Models: In fair machine learning, one source of performance disparities between groups is over-fitting to groups with relatively few training samples. We derive group-specific bounds on the generalization error of welfare-centric fair machine learning that benefit from the larger sample size of the majority group. We do this by considering group-specific Rademacher averages over a restricted hypothesis class, which contains the family of models likely to perform well with respect to a fair learning objective (e.g., a power-mean). Our simulations demonstrate these bounds improve over a naive method, as expected by theory, with particularly significant improvement for smaller group sizes.<|reference_end|>
@article{cousins2024to,
    title={To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair
  Training on Shared Models},
    author={Cyrus Cousins, I. Elizabeth Kumar, Suresh Venkatasubramanian},
    journal={arXiv preprint arXiv:2402.18803},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18803},
    primaryClass={cs.LG cs.CY}
}