2 papers
cs.LG2026
Noise-Aware Framework for Correcting Corrupted Labels
Ha-Linh Nguyen, Hong-Anh Nguyen, Minh-Duc La +4
High-quality labeled data is essential for training reliable ML/DL models. However, real-world datasets often contain a considerable proportion of corrupted labels, which can sever…
cs.LG2026
Structured Exploration and Exploitation of Label Functions for Automated Data Annotation
Phong Lam, Ha-Linh Nguyen, Thu-Trang Nguyen +2
High-quality labeled data is critical for training reliable machine learning and deep learning models, yet manual annotation remains costly and error-prone. Programmatic labeling a…