SUN: Training-free Machine Unlearning via Subspace

Chengyao Qian, Jing Wu, Trung Le, Dinh Phung, Mehrtash Harandi

Keywords: Machine unlearning, Training-free

Abstract:

Machine Unlearning (MU), a technique to erase undesirable content from AI models, plays an essential role in developing safe and trustworthy AI systems. Despite the success MU achieved, existing MU baselines typically necessitate maintaining the entire dataset for fine-tuning unlearned models.
Fine-tuning models and maintaining large datasets are computationally and financially prohibitive. This motivates us to propose a simple yet effective MU approach: \underline{S}ubspace \underline{UN}learning (SUN) as a new fast and effective MU baseline. The proposed method removes the low-dimensional subspaces of undesirable concepts from the space spanned by the weight vectors. This modification makes the model "blind" to the undesirable content to realize unlearning. Notably, SUN can produce the scrubbed model instantly with only a few samples and without additional training.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 786