Conformal-in-the-Loop for Learning with Imbalanced Noisy Data
Conformal-in-the-Loop for Learning with Imbalanced Noisy Data
Class imbalance and label noise are pervasive in large-scale datasets, yet much of machine learning research assumes well-labeled, balanced data, which rarely reflects real world conditions. Existing approaches typically address either label noise or class imbalance in isolation, leading to suboptimal results when both issues coexist. In this work, we …