论文标题
在非参数设置中的标签移位问题的最小最佳方法
Minimax optimal approaches to the label shift problem in non-parametric settings
论文作者
论文摘要
我们研究了非参数分类中标签转移问题的最小值率。除了学习者只能从目标域访问未标记的示例的无监督设置外，我们还考虑了从目标域中可以使用少数标记的示例的设置。我们的研究揭示了这两种设置中标签移位问题的难度有所不同，我们将这种差异归因于目标域的数据可用性，以估计后一种环境中类的条件分布。我们还表明，在无监督的环境中，类比例估计方法是最小值 - 最佳。
We study the minimax rates of the label shift problem in non-parametric classification. In addition to the unsupervised setting in which the learner only has access to unlabeled examples from the target domain, we also consider the setting in which a small number of labeled examples from the target domain is available to the learner. Our study reveals a difference in the difficulty of the label shift problem in the two settings, and we attribute this difference to the availability of data from the target domain to estimate the class conditional distributions in the latter setting. We also show that a class proportion estimation approach is minimax rate-optimal in the unsupervised setting.
