Summary of On Newton’s Method to Unlearn Neural Networks, by Nhung Bui et al.
On Newton’s Method to Unlearn Neural Networks
by Nhung Bui, Xinyang Lu, Rachael Hwee Ling Sim, See-Kiong Ng, Bryan Kian Hsiang Low
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 CureNewton’s method is an approximate unlearning algorithm for neural networks (NNs) that returns identical models to retrained oracles. The goal is to enable individuals to exercise their personal data ownership, particularly the “right to be forgotten” from trained NNs. Newton’s method has been used for linear models but is challenging to adapt for NNs due to degenerate Hessians. The proposed method leverages cubic regularization to handle Hessian degeneracy effectively and eliminates manual finetuning. Experiments show that CureNewton’s method achieves competitive unlearning performance to state-of-the-art algorithms in practical settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning researchers are working on a new way to erase neural networks’ memories. This is important because companies often train these networks using people’s personal data, and individuals should be able to forget their data if they want. The problem is that it takes a lot of computer power to retrain the network from scratch. So, scientists have been looking for ways to “unlearn” neural networks quickly. They tried an old method called Newton’s method but found it didn’t work well with complex networks like NNs. The new CureNewton’s method fixes this by adding some extra math tricks that make it easier and faster. |
Keywords
» Artificial intelligence » Machine learning » Regularization
