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Machine unlearning refers to mechanisms that can remove the influence of asubset of training data upon request from a trained model without incurring thecost of re-training from scratch. This paper develops a unified PAC-Bayesianframework for machine unlearning that recovers the two recent design principles- variational unlearning (Nguyen et.al., 2020) and forgetting Lagrangian(Golatkar et.al., 2020) - as information risk minimization problems(Zhang,2006). Accordingly, both criteria can be interpreted as PAC-Bayesianupper bounds on the test loss of the unlearned model that take the form of freeenergy metrics.

A unified PAC-Bayesian framework for machine unlearning via information risk minimization