






















Abstract:Many high-stakes decisions in health care, public policy, and clinical development require committing to a single policy that will be applied uniformly across a heterogeneous population. Regulatory and fairness standards sometime requires that the chosen policy performs adequately in every pre-specified subpopulation, not only on average. We formalize this as a Selection of the Best with Fairness Constraints (SBFC) problem, in order to identify the policy with the highest average performance among those policies that meet a minimum per-subpopulation threshold. We establish an instance-specific lower bound on sample complexity of the SBFC problem. We then develop a Track-and-Stop with Constraints on Subpopulation (T-a-S-CS) algorithm that achieves the lower bound asymptotically. We extend the framework to general closed-set and penalty-based fairness specifications with matching guarantees. Numerical experiments and a case study using the International Stroke Trial demonstrate substantial efficiency gains over policy-level allocation baselines.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.09945 [cs.LG] |
| (or arXiv:2605.09945v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.09945 arXiv-issued DOI via DataCite (pending registration) |
From: Tingyu Zhu [view email]
[v1]
Mon, 11 May 2026 03:49:38 UTC (242 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。