Extracting Rare Dependence Patterns via Adaptive Sample Reweighting
Li, Yiqing Xia, Yewei Wang, Xiaofei Chen, Zhengming Peng, Liuhua Gong, Mingming Zhang, Kun
Li, Yiqing
Xia, Yewei
Wang, Xiaofei
Chen, Zhengming
Peng, Liuhua
Gong, Mingming
Zhang, Kun
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Research Projects
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Abstract
Discovering dependence patterns between variables from observational data is a fundamental issue in data analysis. However, existing testing methods often fail to detect subtle yet critical patterns that occur within small regions of the data distribution–patterns we term rare dependence. These rare dependencies obscure the true underlying dependence structure in variables, particularly in causal discovery tasks. To address this issue, we propose a novel testing method that combines kernel-based (conditional) independence testing with adaptive sample importance reweighting. By learning and assigning higher importance weights to data points exhibiting significant dependence, our method amplifies the dependence patterns and detects them successfully. Theoretically, we analyze the asymptotic distributions of the statistics in this method and show the uniform bound of the learning scheme. Furthermore, we integrate our tests into the PC algorithm, a constraint-based approach for causal discovery, equipping it to uncover causal relationships even in the presence of rare dependence. Empirical evaluation of synthetic and real-world datasets comprehensively demonstrates the efficacy of our method.
Citation
Y. Li et al., “Extracting Rare Dependence Patterns via Adaptive Sample Reweighting,” Oct. 06, 2025, PMLR. [Online]. Available: https://proceedings.mlr.press/v267/li25da.html
Source
Proceedings of Machine Learning Research
Conference
42nd International Conference on Machine Learning, ICML 2025
Keywords
Subjects
Source
42nd International Conference on Machine Learning, ICML 2025
Publisher
ML Research Press