Label Recovery

Label recovery focuses on reconstructing missing or corrupted labels from various data sources, aiming to improve the robustness and accuracy of machine learning models. Current research emphasizes developing algorithms that handle hybrid noise (corrupted features and labels), particularly within federated learning settings where recovering labels from model updates is crucial for privacy attacks. Methods often involve optimization techniques like low-rank approximation and ADMM, or leverage graph matching and clustering to recover labels from related, labeled data. These advancements have implications for improving data quality, enhancing model security, and advancing understanding of structured prediction problems.