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2024

Loss-Free Machine Unlearning

Foster, Jack, Schoepf, Stefan, Brintrup, Alexandra

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We present a machine unlearning approach that is both retraining- and label-free.

Most existing machine unlearning approaches require a model to be fine-tuned to remove information while preserving performance.
This is computationally expensive and necessitates the storage of the whole dataset for the lifetime of the model.
Retraining-free approaches often utilise Fisher information, which is derived from the loss and requires labelled data which may not be available.

Loss-Free Machine Unlearning · Around

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