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cs.LG updates on arXiv.org

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Exact Unlearning from Proxies Induces Closeness Guarantee...
Virgile Dine · 2026-05-12 · via cs.LG updates on arXiv.org

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Abstract:This paper proposes a paradigm shift linking machine unlearning directly to the structure of the data distributions rather than a mere update of the neural network parameters. We show that inferring these distributions with precision enables distilling the exact unlearning signal induced by the modeling. Theoretical bounds on the Kullback-Leibler divergence from the ideal retrained model to our unlearned model, under verifiable admissibility criterion, reveal the soundness of our framework. This method is experimentally validated over three forgetting scenarios as reaching the closest classifier to the ideal retrained model when compared to competitors.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2605.10680 [cs.LG]
  (or arXiv:2605.10680v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2605.10680

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Virgile Dine [view email]
[v1] Mon, 11 May 2026 14:57:31 UTC (273 KB)