We present a study of surrogate losses and algorithms for the general problem of learning to defer with multiple experts. We first introduce a new family of surrogate losses specifically tailored for the multiple-expert setting, where the prediction and deferral functions are learned simultaneously. We then prove that these surrogate losses benefit from strong $H$-consistency bounds. We illustrate the application of our analysis through several examples of practical surrogate losses, for which we give explicit guarantees. These loss functions readily lead to the design of new learning to defer algorithms based on their minimization. While the main focus of this work is a theoretical analysis, we also report the results of several experiments on SVHN and CIFAR-10 datasets.

翻译：本文针对多专家场景下学习延迟决策的通用问题，系统研究了替代损失函数与相应算法。我们首先提出一类专为多专家设置设计的新型替代损失函数，其中预测与延迟决策函数可同步学习。随后证明这些替代损失函数具有强$H$-一致性界。通过多个实用替代损失函数的实例分析（同时给出显式保证），阐释了理论框架的应用。基于这些损失函数的最小化过程，我们可直接推导出新型学习延迟决策算法。尽管本研究主要致力于理论分析，但仍报告了在SVHN和CIFAR-10数据集上的多组实验结果。