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：我们针对多专家场景下的延迟学习通用问题，提出了一系列替代性损失函数与算法研究。首先，我们提出了专门为多专家场景设计的新型替代性损失函数族，该函数族能够同时学习预测与延迟决策函数。随后我们证明了这些替代损失函数具有强$H$一致性边界。通过多个实用替代损失函数的实例分析，我们给出了具体的理论保证。这些损失函数可直接指导基于其最小化原则的新延迟学习算法设计。尽管本研究的核心是理论分析，我们亦在SVHN与CIFAR-10数据集上报告了多项实验结果。