Spectral Saliency for Machine Unlearning
Machine unlearning (MU) aims to remove the influence of specific training data while preserving model utility. As the name suggests, MU can be viewed as the inverse of learning, using gradient-based updates to reduce the influence of a forget-set by counteracting the previously learned behavior. Recently, Muon, a gradient descent variant, has been introduced. Muon applies spectral magnitude normalization to encourage exploration of rare directions and demonstrates promising performance. Inspired by Muon, we adopt the spectral view for unlearning and propose Spectral Saliency Unlearning (SSU). SSU thresholds weak singular components and updates only those directions supported by a confiden...
| Authors | Cedar Site Bai, Amber Yijia Zheng, Raymond A. Yeh et al. |
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| Primary category | cs.LG |
| Published | 16 August 2026 |
| Last updated at source | 26 September 2026 |
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