ICML26: Asymmetric Sources Unlearning Improves Privacy-Utility Tradeoff
gkdziugaite · x · 2026-07-07
At ICML 2026 in Seoul, researchers presented the paper "Unlearning with Asymmetric Sources," proposing the use of public data to improve the tradeoff between privacy certification and model utility in Machine Unlearning. Machine unlearning aims to make models forget specific training data and is a key technology for AI privacy compliance. This study provides a new theoretical framework for large-scale unlearning scenarios.
Related event: ICML 2026 Paper Proposes ALU Framework for Large-Scale Machine Unlearning(5 posts)→
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