Learning from label proportions (LLP) is a weakly supervised classification problem where data points are grouped into bags, and the label proportions within each bag are observed instead of the instance-level labels. The task is to learn a classifier to predict the labels of future individual instances. Prior work on LLP for multi-class data has yet to develop a theoretically grounded algorithm. In this work, we propose an approach to LLP based on a reduction to learning with label noise, using the forward correction (FC) loss of Patrini et al. [30]. We establish an excess risk bound and generalization error analysis for our approach, while also extending the theory of the FC loss which may be of independent interest. Our approach demonstrates improved empirical performance in deep learning scenarios across multiple datasets and architectures, compared to the leading methods.
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Free, publicly-accessible full text available January 4, 2024
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Learning from label proportions (LLP) is a weakly supervised setting for classification in which unlabeled training instances are grouped into bags, and each bag is annotated with the proportion of each class occurring in that bag. Prior work on LLP has yet to establish a consistent learning procedure, nor does there exist a theoretically justified, general purpose training criterion. In this work we address these two issues by posing LLP in terms of mutual contamination models (MCMs), which have recently been applied successfully to study various other weak supervision settings. In the process, we establish several novel technical results for MCMs, including unbiased losses and generalization error bounds under non-iid sampling plans. We also point out the limitations of a common experimental setting for LLP, and propose a new one based on our MCM framework.
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Learning from label proportions (LLP) is a weakly supervised setting for classification in whichunlabeled training instances are grouped into bags, and each bag is annotated with the proportion ofeach class occurring in that bag. Prior work on LLP has yet to establish a consistent learning procedure,nor does there exist a theoretically justified, general purpose training criterion. In this work we addressthese two issues by posing LLP in terms of mutual contamination models (MCMs), which have recentlybeen applied successfully to study various other weak supervision settings. In the process, we establishseveral novel technical results for MCMs, including unbiased losses and generalization error bounds undernon-iid sampling plans. We also point out the limitations ofa common experimental setting for LLP,and propose a new one based on our MCM framework.
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