论文标题
在标签轮班下积极学习
Active Learning under Label Shift
论文作者
论文摘要
我们解决了标签偏移下的主动学习问题：当源和目标域的类比例不同时。我们引入了“中间分布”，以结合重要性加权与级别平衡抽样之间的权衡，并提出了他们在积极学习中的综合用法。我们的方法被称为标签转移（购物中心）下的介导的主动学习。它可以平衡级别平衡抽样的偏见和重要性权重的差异。我们证明，即使在任意标签转移下，我们也证明了表现出活跃学习的购物中心的样本复杂性和泛化保证，这些杂志也会降低渐近样品的复杂性。我们从经验上展示了购物中心量表到高维数据集，并且可以在深度活跃的学习任务中减少60％的主动学习的样本复杂性。
We address the problem of active learning under label shift: when the class proportions of source and target domains differ. We introduce a "medial distribution" to incorporate a tradeoff between importance weighting and class-balanced sampling and propose their combined usage in active learning. Our method is known as Mediated Active Learning under Label Shift (MALLS). It balances the bias from class-balanced sampling and the variance from importance weighting. We prove sample complexity and generalization guarantees for MALLS which show active learning reduces asymptotic sample complexity even under arbitrary label shift. We empirically demonstrate MALLS scales to high-dimensional datasets and can reduce the sample complexity of active learning by 60% in deep active learning tasks.
