Summary of Certified Machine Unlearning Via Noisy Stochastic Gradient Descent, by Eli Chien et al.
Certified Machine Unlearning via Noisy Stochastic Gradient Descent
by Eli Chien, Haoyu Wang, Ziang Chen, Pan Li
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 This paper proposes a novel approach to machine unlearning, which aims to efficiently remove the influence of certain data points on trained model parameters. The authors leverage projected noisy stochastic gradient descent (PNGD) for unlearning and establish a first approximate unlearning guarantee under convexity assumptions. This approach offers several benefits, including provable complexity savings compared to retraining and support for sequential and batch unlearning. The paper also provides new results on tracking the infinite Wasserstein distance between adjacent learning processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that machine learning models don’t remember things they’re not supposed to. This is important because it helps keep our personal data private. The authors came up with a way to make machines “forget” certain information, which can be useful in many situations. Their method is called projected noisy stochastic gradient descent (PNGD), and it’s more efficient than retraining the model from scratch. This means that we can get similar results without having to do as much work. |
Keywords
* Artificial intelligence * Machine learning * Stochastic gradient descent * Tracking
