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.