Estimation of the Label-Noise Transition Matrix with Performance Guarantees via Selective Classification

Abstract:Modern machine learning depends heavily on massive datasets, but obtaining high-quality annotations at scale is often expensive. As a result, learning from noisily-labeled data has become common, making accurate estimation of the label-noise transition matrix crucial. However, existing transition matrix estimators rely on the fragile estimation of class-posteriors and do not provide finite-sample performance guarantees. In this work, we propose a novel methodology to estimate the transition matrix based on one-sided selective classification. This approach bypasses class-posterior estimation, provides finite-sample performance guarantees, and leverages flexible learning methods for binary classification. Moreover, we introduce effective algorithms to implement the proposed methodology and provide their refined finite-sample performance bounds.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2609.39829 [stat.ML]

(or arXiv:2609.39829v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2609.39829 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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