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<p>In this paper, we introduce a robust transfer regression method designed to handle corrupted labels in target data, under the scenarios that the corruption affects a substantial portion of the labels and the locations of these corruptions are unknown. Our theoretical analysis decomposes the estimation error into three interpretable components: (1) source data, (2) domain shift, and (3) label corruption. This framework guarantees that our method consistently outperforms target-only estimation. We validate our method through numerical experiments focused on reconstructing corrupted compressed signals, showing robustness even when a high fraction of labels are corrupted, especially when some source data exhibit structural similarities to the target data. Additionally, we apply our method to analyze the association between O6-methylguanine-DNA methyltransferase (MGMT) methylation and gene expression in glioblastoma (GBM) patients.</p>

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Robust Transfer Regression with Corrupted Labels

  • 期刊论文

  • 发表日期：

    2026年6月26日

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  • Sheng Pan

摘要

In this paper, we introduce a robust transfer regression method designed to handle corrupted labels in target data, under the scenarios that the corruption affects a substantial portion of the labels and the locations of these corruptions are unknown. Our theoretical analysis decomposes the estimation error into three interpretable components: (1) source data, (2) domain shift, and (3) label corruption. This framework guarantees that our method consistently outperforms target-only estimation. We validate our method through numerical experiments focused on reconstructing corrupted compressed signals, showing robustness even when a high fraction of labels are corrupted, especially when some source data exhibit structural similarities to the target data. Additionally, we apply our method to analyze the association between O6-methylguanine-DNA methyltransferase (MGMT) methylation and gene expression in glioblastoma (GBM) patients.