论文标题
从歧视功能反馈中学习的强大学习
Robust Learning from Discriminative Feature Feedback
论文作者
论文摘要
最近的工作介绍了从判别特征反馈中学习的模型，其中人注释不仅提供了实例标签，而且还标识了判别特征，这些特征突出了一对实例之间的重要差异。结果表明，这种反馈可以有利于学习，并可以有效地学习一些概念课，否则这些概念类别是棘手的。但是，这些结果都依赖于完美的注释反馈。在本文中，我们介绍了框架的更现实，更强大的版本，其中允许注释者犯错误。我们在对抗性和随机设置中都可以展示如何在算法上处理此类错误。特别是，我们在这两种设置中都会得出后悔的界限，因为在完美注释者的情况下，它们独立于功能数量。我们表明，从鲁棒设置到非舒适设置的幼稚减少无法获得该结果。
Recent work introduced the model of learning from discriminative feature feedback, in which a human annotator not only provides labels of instances, but also identifies discriminative features that highlight important differences between pairs of instances. It was shown that such feedback can be conducive to learning, and makes it possible to efficiently learn some concept classes that would otherwise be intractable. However, these results all relied upon perfect annotator feedback. In this paper, we introduce a more realistic, robust version of the framework, in which the annotator is allowed to make mistakes. We show how such errors can be handled algorithmically, in both an adversarial and a stochastic setting. In particular, we derive regret bounds in both settings that, as in the case of a perfect annotator, are independent of the number of features. We show that this result cannot be obtained by a naive reduction from the robust setting to the non-robust setting.
