Group Loss

Group loss in machine learning focuses on optimizing model performance across different subgroups or data partitions, addressing issues like data heterogeneity, bias, and label noise. Current research explores various approaches, including multi-objective optimization, loss decomposition, and group-aware distillation, often within the context of federated learning or deep metric learning, to achieve fairer and more robust models. This research is significant because it tackles critical challenges in building reliable and equitable AI systems, impacting fields ranging from medical imaging to online recommendation systems. The development of effective group loss methods is crucial for improving the generalizability and fairness of machine learning models.