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Summary of Label Smoothing Improves Machine Unlearning, by Zonglin Di et al.


Label Smoothing Improves Machine Unlearning

by Zonglin Di, Zhaowei Zhu, Jinghan Jia, Jiancheng Liu, Zafar Takhirov, Bo Jiang, Yuanshun Yao, Sijia Liu, Yang Liu

First submitted to arxiv on: 11 Jun 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: None

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GrooveSquid.com Paper Summaries

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The paper proposes a new machine unlearning (MU) approach called UGradSL, which eliminates previously learned data from a model while minimizing computation cost and maintaining performance. Inspired by label smoothing and differential privacy, UGradSL uses an inverse process of label smoothing to achieve better MU results. Theoretical analyses show that properly introducing label smoothing improves MU performance. Experimental results on six datasets demonstrate the effectiveness and robustness of UGradSL, achieving up to 66% improvement in unlearning accuracy with minimal additional computation.
Low GrooveSquid.com (original content) Low Difficulty Summary
Machine unlearning (MU) is a way to remove old information from a computer model. The goal is to balance how much computing power is needed with how well the model works after unlearning. A new method called UGradSL helps achieve this balance by using “smoothed labels”. This means that the old information is gradually erased, not suddenly removed. The paper shows that UGradSL is a simple and effective way to unlearn information from a model.

Keywords

» Artificial intelligence