Computer Science > Machine Learning
arXiv:2207.06687 (cs)
[Submitted on 14 Jul 2022 (v1), last revised 24 Feb 2023 (this version, v2)]
Title:Breaking Correlation Shift via Conditional Invariant Regularizer
View a PDF of the paper titled Breaking Correlation Shift via Conditional Invariant Regularizer, by Mingyang Yi and Ruoyu Wang and Jiachen Sun and Zhenguo Li and Zhi-Ming Ma
View PDFAbstract:Recently, generalization on out-of-distribution (OOD) data with correlation shift has attracted great attentions. The correlation shift is caused by the spurious attributes that correlate to the class label, as the correlation between them may vary in training and test data. For such a problem, we show that given the class label, the models that are conditionally independent of spurious attributes are OOD generalizable. Based on this, a metric Conditional Spurious Variation (CSV) which controls the OOD generalization error, is proposed to measure such conditional independence. To improve the OOD generalization, we regularize the training process with the proposed CSV. Under mild assumptions, our training objective can be formulated as a nonconvex-concave mini-max problem. An algorithm with a provable convergence rate is proposed to solve the problem. Extensive empirical results verify our algorithm's efficacy in improving OOD generalization.
| Comments: | Published in ICLR-2023 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2207.06687 [cs.LG] |
| (orarXiv:2207.06687v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2207.06687 arXiv-issued DOI via DataCite |
Submission history
From: Mingyang Yi [view email][v1] Thu, 14 Jul 2022 06:34:21 UTC (877 KB)
[v2] Fri, 24 Feb 2023 10:30:29 UTC (2,187 KB)
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View a PDF of the paper titled Breaking Correlation Shift via Conditional Invariant Regularizer, by Mingyang Yi and Ruoyu Wang and Jiachen Sun and Zhenguo Li and Zhi-Ming Ma
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