Evaluating Model Performance Under Worst-case Subpopulations

• URL: http://arxiv.org/abs/2407.01316v1
• Date: Mon, 1 Jul 2024 14:24:05 GMT
• Title: Evaluating Model Performance Under Worst-case Subpopulations
• Authors: Mike Li, Hongseok Namkoong, Shangzhou Xia,
• Abstract summary: We study the worst-case performance of a model over all subpopulations of a given size, defined with respect to core attributes Z. We develop a scalable yet principled two-stage estimation procedure that can evaluate the robustness of state-of-the-art models.
• Score: 8.615300901890253
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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