Estimation of the Label-Noise Transition Matrix with Performance Guarantees via Selective Classification
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 ...
| Authors | Xabier de Juan, Santiago Mazuelas, Yilun Zhu et al. |
|---|---|
| Primary category | stat.ML |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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