Robust Label Shift Quantification
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914320280977408 |
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| author | Lecestre, Alexandre |
| author_facet | Lecestre, Alexandre |
| contents | In this paper, we investigate the label shift quantification problem. We propose robust estimators of the label distribution which turn out to coincide with the Maximum Likelihood Estimator. We analyze the theoretical aspects and derive deviation bounds for the proposed method, providing optimal guarantees in the well-specified case, along with notable robustness properties against outliers and contamination. Our results provide theoretical validation for empirical observations on the robustness of Maximum Likelihood Label Shift. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_03174 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust Label Shift Quantification Lecestre, Alexandre Statistics Theory Machine Learning 62F35 In this paper, we investigate the label shift quantification problem. We propose robust estimators of the label distribution which turn out to coincide with the Maximum Likelihood Estimator. We analyze the theoretical aspects and derive deviation bounds for the proposed method, providing optimal guarantees in the well-specified case, along with notable robustness properties against outliers and contamination. Our results provide theoretical validation for empirical observations on the robustness of Maximum Likelihood Label Shift. |
| title | Robust Label Shift Quantification |
| topic | Statistics Theory Machine Learning 62F35 |
| url | https://arxiv.org/abs/2502.03174 |