论文标题
对补充人类的预测指标的有效学习
Sample Efficient Learning of Predictors that Complement Humans
论文作者
论文摘要
学习算法的目标之一是补充和减轻人类决策者的负担。算法可以自行预测的专家延期设置，也可以将决定推迟到下游专家有助于实现这一目标。这种环境的一个基本方面是需要学习改善人类弱点的互补预测因子，而不是学习预测因素以优化平均错误。在这项工作中，我们提供了第一个理论分析，以了解专家延期中学习补充预测指标的好处。为了有效地学习此类预测因素，我们考虑了一个一致的替代损失功能的家族，以延期专家并分析其理论特性。最后，我们设计了需要最少人类专家预测数据的积极学习方案，以学习准确的延期系统。
One of the goals of learning algorithms is to complement and reduce the burden on human decision makers. The expert deferral setting wherein an algorithm can either predict on its own or defer the decision to a downstream expert helps accomplish this goal. A fundamental aspect of this setting is the need to learn complementary predictors that improve on the human's weaknesses rather than learning predictors optimized for average error. In this work, we provide the first theoretical analysis of the benefit of learning complementary predictors in expert deferral. To enable efficiently learning such predictors, we consider a family of consistent surrogate loss functions for expert deferral and analyze their theoretical properties. Finally, we design active learning schemes that require minimal amount of data of human expert predictions in order to learn accurate deferral systems.
