Evaluating Model Performance Under Worst-case Subpopulations
<|reference_start|>Evaluating Model Performance Under Worst-case Subpopulations: The performance of ML models degrades when the training population is different from that seen under operation. Towards assessing distributional robustness, we study the worst-case performance of a model over all subpopulations of a given size, defined with respect to core attributes Z. This notion of robustness can consider arbitrary (continuous) attributes Z, and automatically accounts for complex intersectionality in disadvantaged groups. We develop a scalable yet principled two-stage estimation procedure that can evaluate the robustness of state-of-the-art models. We prove that our procedure enjoys several finite-sample convergence guarantees, including dimension-free convergence. Instead of overly conservative notions based on Rademacher complexities, our evaluation error depends on the dimension of Z only through the out-of-sample error in estimating the performance conditional on Z. On real datasets, we demonstrate that our method certifies the robustness of a model and prevents deployment of unreliable models.<|reference_end|>
@article{li2024evaluating,
    title={Evaluating Model Performance Under Worst-case Subpopulations},
    author={Mike Li, Hongseok Namkoong, Shangzhou Xia},
    journal={arXiv preprint arXiv:2407.01316},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01316},
    primaryClass={cs.LG cs.CY stat.ML}
}