Estimation of the Label-Noise Transition Matrix with Performance Guarantees via Selective Classification
The paper proposes estimating label-noise transition matrices by treating each column estimation as a one-sided selective classification problem. This approach minimizes false discovery rate while ensuring minimum coverage, leading to finite-sample performance guarantees and avoiding the curse of dimensionality found in existing methods.