Supervised machine learning often requires human-annotated data. While annotator disagreement is typically interpreted as evidence of noise, population-level label distribution learning (PLDL) treats the collection of annotations for each data item as a sample of the opinions of a population of human annotators, among whom disagreement may be proper and expected, even with no noise present. From this perspective, a typical training set may contain a large number of very small-sized samples, one for each data item, none of which, by itself, is large enough to be considered representative of the underlying population's beliefs about that item. We propose an algorithmic framework and new statistical tests for PLDL that account for sampling size. We apply them to previously proposed methods for sharing labels across similar data items. We also propose new approaches for label sharing, which we call neighborhood-based pooling.

中文翻译：

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用于人口级标签分布学习的基于邻域的池化

有监督的机器学习通常需要人工标注的数据。虽然注释者的分歧通常被解释为噪音的证据，但群体级标签分布学习 (PLDL) 将每个数据项的注释集合视为人类注释者群体意见的样本，其中的分歧可能是正确的和预期的，即使没有噪音。从这个角度来看，典型的训练集可能包含大量非常小的样本，每个数据项一个，这些样本本身都不足以被视为代表潜在人群对该项目的信念。我们为 PLDL 提出了一个算法框架和新的统计测试，以考虑抽样规模。我们将它们应用于先前提出的跨相似数据项共享标签的方法。