Machine Unlearning via Null Space Calibration
Date
2024
Authors
Chen, H.
Zhu, T.
Yu, X.
Zhou, W.
Editors
Larson, K.
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Conference paper
Citation
IJCAI International Joint Conference on Artificial Intelligence, 2024 / Larson, K. (ed./s), pp.358-366
Statement of Responsibility
Huiqiang Chen, Tianqing Zhu, Xin Yu, Wanlei Zhou
Conference Name
International Joint Conference on Artificial Intelligence (IJCAI) (3 Aug 2024 - 9 Aug 2024 : Jeju, South Korea)
Abstract
Machine unlearning aims to enable models to forget specific data instances when receiving deletion requests. Current research centers on efficient unlearning to erase the influence of data from the model and neglects the subsequent impacts on the remaining data. Consequently, existing unlearning algorithms degrade the model's performance after unlearning, known as over-unlearning. This paper addresses this critical yet under-explored issue by introducing machine Unlearning via Null Space Calibration (UNSC), which can accurately unlearn target samples without over-unlearning. On the contrary, by calibrating the decision space during unlearning, UNSC can significantly improve the model's performance on the remaining samples. In particular, our approach hinges on confining the unlearning process to a specified null space tailored to the remaining samples, which is augmented by strategically pseudo-labeling the unlearning samples. Comparison against several established baselines affirms the superiority of our approach.
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