Many real-world multi-label prediction problems involve set-valued predictions that must satisfy specific requirements dictated by downstream usage. We focus on a typical scenario where such requirements, separately encoding \textit{value} and \textit{cost}, compete with each other. For instance, a hospital might expect a smart diagnosis system to capture as many severe, often co-morbid, diseases as possible (the value), while maintaining strict control over incorrect predictions (the cost). We present a general pipeline, dubbed as FavMac, to maximize the value while controlling the cost in such scenarios. FavMac can be combined with almost any multi-label classifier, affording distribution-free theoretical guarantees on cost control. Moreover, unlike prior works, FavMac can handle real-world large-scale applications via a carefully designed online update mechanism, which is of independent interest. Our methodological and theoretical contributions are supported by experiments on several healthcare tasks and synthetic datasets - FavMac furnishes higher value compared with several variants and baselines while maintaining strict cost control.

翻译：许多现实中的多标签预测问题涉及必须满足下游使用特定要求的集值预测。我们关注一个典型场景，其中这些要求分别编码\textit{价值}和\textit{成本}，并相互竞争。例如，医院可能期望智能诊断系统捕获尽可能多的严重且常共存的疾病（价值），同时严格控制错误预测（成本）。我们提出一个通用流程，称为FavMac，旨在此类场景中最大化价值并控制成本。FavMac可与几乎任何多标签分类器结合，提供无分布假设的成本控制理论保证。此外，与先前工作不同，FavMac通过精心设计的在线更新机制处理现实中的大规模应用，该机制本身具有独立研究价值。我们的方法和理论贡献通过多个医疗任务与合成数据集的实验得到支持——在保持严格成本控制的同时，FavMac相比多种变体和基线提供了更高的价值。