@inproceedings{
chen2024imprecise,
title={Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations},
author={Hao Chen and Ankit Shah and Jindong Wang and Ran Tao and Yidong Wang and Xiang Li and Xing Xie and Masashi Sugiyama and Rita Singh and Bhiksha Raj},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0Lr9HQijA1}
}
Learning with reduced labeling standards, such as noisy label, partial label, and supplementary unlabeled data, which we generically refer to as imprecise label, is a commonplace challenge in machine learning tasks. Previous methods tend to propose specific designs for every emerging imprecise label configuration, which is usually unsustainable when multiple configurations of imprecision coexist. 
In this paper, we introduce imprecise label learning (ILL), a framework for the unification of learning with various imprecise label configurations. ILL leverages expectation-maximization (EM) for modeling the imprecise label information, treating the precise labels as latent variables. Instead of approximating the correct labels for training, it considers the entire distribution of all possible labeling entailed by the imprecise information. We demonstrate that ILL can seamlessly adapt to partial label learning, semi-supervised learning, noisy label learning, and, more importantly, a mixture of these settings, with closed-form learning objectives derived from the unified EM modeling. Notably, ILL surpasses the existing specified techniques for handling imprecise labels, marking the first practical and unified framework with robust and effective performance across various challenging settings. We hope our work will inspire further research on this topic, unleashing the full potential of ILL in wider scenarios where precise labels are expensive and complicated to obtain.
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
Hao Chen Ankit Shah Jindong Wang Ran Tao Yidong Wang Xiang Li Xing Xie Masashi Sugiyama Rita Singh Bhiksha Raj