Multilabel ranking is a central task in machine learning. However, the most fundamental question of learnability in a multilabel ranking setting with relevance-score feedback remains unanswered. In this work, we characterize the learnability of multilabel ranking problems in both batch and online settings for a large family of ranking losses. Along the way, we give two equivalence classes of ranking losses based on learnability that capture most, if not all, losses used in practice.

翻译：多标签排名是机器学习中的核心任务。然而，在具有相关性分数反馈的多标签排名场景中，最基本的可学习性问题仍未得到解答。在本工作中，我们刻画了批处理与在线设置下多标签排名问题的可学习性，研究对象涵盖一大类排名损失函数。在此过程中，我们基于可学习性给出了排名损失函数的两个等价类，这些类涵盖了实践中使用的大部分（若非全部）损失函数。