论文标题
一致的估计器学习推迟专家
Consistent Estimators for Learning to Defer to an Expert
论文作者
论文摘要
在实际情况下，学习算法通常与专家决策者结合使用，但是在设计这些算法时，这一事实在很大程度上被忽略了。在本文中，我们探讨了如何学习可以预测或选择将决定推迟给下游专家的预测因素。只有专家决策的样本，我们提供了一个基于学习分类器和拒绝者的程序，并从理论上进行分析。我们的方法基于一种新颖的减少，以使成本敏感学习为成本敏感学习提供一致的替代损失，从而概括了跨熵损失。我们显示了方法对各种实验任务的有效性。
Learning algorithms are often used in conjunction with expert decision makers in practical scenarios, however this fact is largely ignored when designing these algorithms. In this paper we explore how to learn predictors that can either predict or choose to defer the decision to a downstream expert. Given only samples of the expert's decisions, we give a procedure based on learning a classifier and a rejector and analyze it theoretically. Our approach is based on a novel reduction to cost sensitive learning where we give a consistent surrogate loss for cost sensitive learning that generalizes the cross entropy loss. We show the effectiveness of our approach on a variety of experimental tasks.
