New framework for auditing machine unlearning

A new framework called Regularized f-Divergence Kernel Tests, presented at AISTATS 2026, makes auditing machine unlearning more sensitive and accurate. The method uses two-sample testing to verify if a model truly forgot specific training data, addressing computational costs and statistical power loss in large models.