Similarity-Aware Machine Unlearning
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Computer Science > Machine Learning
Title:Similarity-Aware Machine Unlearning
Abstract:Machine unlearning removes the influence of user-specified training examples from a trained model, avoiding the need to retrain it from scratch. Localization-based methods improve unlearning efficiency by identifying a subset of influential model parameters. However, existing approaches select parameters based solely on forget-set importance, neglecting their role in retained dataset and often causing collateral damage to semantically similar retained examples. We address this limitation with a retain-aware localization method that considers parameter importance to both forgotten and retained data. We also introduce a retain-similar evaluation set, constructed using cosine similarity in the model embedding space, to directly measure collateral damage. Across eleven experimental settings on CIFAR-10 dataset and ResNet18 model, our method consistently reduces collateral damage while improving standard unlearning metrics, demonstrating the effectiveness of retain-aware localization for similarity-aware machine unlearning.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.00246 [cs.LG] |
| (or arXiv:2608.00246v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.00246
arXiv-issued DOI via DataCite
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Submission history
From: Haripriya Harikumar [view email][v1] Fri, 31 Jul 2026 19:41:55 UTC (1,395 KB)
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