Summary of Towards Certified Unlearning For Deep Neural Networks, by Binchi Zhang et al.
Towards Certified Unlearning for Deep Neural Networks
by Binchi Zhang, Yushun Dong, Tianhao Wang, Jundong Li
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 proposed methods extend certified unlearning to deep neural networks (DNNs) by employing simple techniques to adapt convex-certified unlearning approaches to nonconvex objectives. The approach leverages inverse Hessian approximation to efficiently compute certification guarantees while maintaining strong theoretical properties. Notably, the method is applied to nonconvergence training and sequential unlearning, allowing for real-world unlearning requests at various time points. Experimental results on three datasets demonstrate the effectiveness of the proposed method and its advantages in certified unlearning for DNNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make deep learning machines forget specific things they learned, but with a guarantee that they won’t start remembering bad things again. This is important because people might want to “unlearn” something their AI model learned before. The researchers found ways to do this efficiently and safely for complex neural networks used in many applications today. |
Keywords
» Artificial intelligence » Deep learning
