从总体观察中学习
Learning from Aggregate Observations
摘要：
我们研究了从聚合观测中学习的问题，其中监督信号被给予实例集而不是单个实例，而目标仍然是预测未见过的个体的标签。一个著名的例子是多实例学习（MIL）。在本文中，我们扩展MIL超出二进制分类的其他问题，如多类分类和回归。我们提出了一个通用的概率框架，可容纳各种聚合观测，例如，用于分类的成对相似性/三联体比较和用于回归的平均值/差异/秩观察。简单的极大似然解可以应用于各种可微模型，例如深度神经网络和梯度提升机。此外，我们发展的概念一致性的等价关系来描述我们的估计，并表明它有很好的收敛性在温和的假设下。三个问题的设置-分类通过三元组比较和回归通过均值/秩观察的实验表明，所提出的方法的有效性。
英文摘要：
We study the problem of learning from aggregate observations where supervision signals are given to sets of instances instead of individual instances, while the goal is still to predict labels of unseen individuals. A well-known example is multiple instance learning (MIL). In this paper, we extend MIL beyond binary classification to other problems such as multiclass classification and regression. We present a general probabilistic framework that accommodates a variety of aggregate observations, e.g., pairwise similarity/triplet comparison for classification and mean/difference/rank observation for regression. Simple maximum likelihood solutions can be applied to various differentiable models such as deep neural networks and gradient boosting machines. Moreover, we develop the concept of consistency up to an equivalence relation to characterize our estimator and show that it has nice convergence properties under mild assumptions. Experiments on three problem settings -- classification via triplet comparison and regression via mean/rank observation indicate the effectiveness of the proposed method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
