Review for NeurIPS paper: Variational Bayesian Unlearning

Given that the method is only approximate and forgetting a specific data point cannot be theoretically guaranteed, can the authors comment on how practically applicable the proposed approach is? Or are the GDPR requirements so strict as to require retraining/proofed forgetting to be fulfilled making the paper a nice first step, but leaving lots of further problems until the formal requirements are met? - l67 and others refer to the Kullback Leibler divergence as a distance. Given that it is not a distance due to its lack of symmetry it should properly be called divergence or relative entropy.