Nonparametric Identification of Latent Concepts
Zheng, Yujia Xie, Shaoan Zhang, Kun
Zheng, Yujia
Xie, Shaoan
Zhang, Kun
Author
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
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Language
English
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Abstract
We are born with the ability to learn concepts by comparing diverse observations. This helps us to understand the new world in a compositional manner and facilitates extrapolation, as objects naturally consist of multiple concepts. In this work, we argue that the cognitive mechanism of comparison, fundamental to human learning, is also vital for machines to recover true concepts underlying the data. This offers correctness guarantees for the field of concept learning, which, despite its impressive empirical successes, still lacks general theoretical support. Specifically, we aim to develop a theoretical framework for the identifiability of concepts with multiple classes of observations. We show that with sufficient diversity across classes, hidden concepts can be identified without assuming specific concept types, functional relations, or parametric generative models. Interestingly, even when conditions are not globally satisfied, we can still provide alternative guarantees for as many concepts as possible based on local comparisons, thereby extending the applicability of our theory to more flexible scenarios. Moreover, the hidden structure between classes and concepts can also be identified nonparametrically. We validate our theoretical results in both synthetic and real-world settings.
Citation
Y. Zheng, S. Xie, and K. Zhang, “Nonparametric Identification of Latent Concepts,” Proc Mach Learn Res, 2025
Source
Proceedings of Machine Learning Research
Conference
42nd International Conference on Machine Learning, ICML 2025
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Source
42nd International Conference on Machine Learning, ICML 2025
Publisher
ML Research Press