GRIN+ Method Targets Machine Unlearning in Imbalanced Medical Data

A new arXiv preprint introduces GRIN+, a machine unlearning approach aimed at removing patient data from trained medical models both quickly and effectively. The work focuses on imbalanced clinical datasets, a setting where existing unlearning methods tend to degrade model performance. It frames the problem around privacy rules such as GDPR and HIPAA that grant patients the right to have their data erased.