On the Impossibility of Retrain Equivalence in Machine Unlearning
NeutralArtificial Intelligence
A recent paper discusses the challenges of achieving Retrain Equivalence in machine unlearning, which aims to erase the influence of specific training data from a model. This concept, initially designed for models trained on independent and identically distributed data, faces complications in modern multi-stage training environments where data distributions and objectives vary. Understanding these limitations is crucial as it impacts the development of more effective machine learning models.
— Curated by the World Pulse Now AI Editorial System

