Summary of Understanding Fine-tuning in Approximate Unlearning: a Theoretical Perspective, by Meng Ding et al.
Understanding Fine-tuning in Approximate Unlearning: A Theoretical Perspective
by Meng Ding, Rohan Sharma, Changyou Chen, Jinhui Xu, Kaiyi Ji
First submitted to arxiv on: 4 Oct 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 Machine Unlearning has emerged as a significant area of research, focusing on removing specific subsets of data from a trained model. Fine-tuning (FT) methods have become one of the fundamental approaches for approximating unlearning, as they effectively retain model performance. However, it is consistently observed that naive FT methods struggle to forget the targeted data. This paper presents the first theoretical analysis of FT methods for machine unlearning within a linear regression framework, providing a deeper exploration of this phenomenon. The authors reveal that while FT models can achieve zero remaining loss, they fail to forget the forgetting data, as the pretrained model retains its influence and the fine-tuning process does not adequately mitigate it. To address this, the authors propose a novel Retention-Based Masking (RBM) strategy that constructs a weight saliency map based on the remaining dataset, unlike existing methods that focus on the forgetting dataset. The theoretical analysis demonstrates that RBM not only significantly improves unlearning accuracy (UA) but also ensures higher retaining accuracy (RA) by preserving overlapping features shared between the forgetting and remaining datasets. Experiments on synthetic and real-world datasets validate the authors’ theoretical insights, showing that RBM outperforms existing masking approaches in balancing UA, RA, and disparity metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine Unlearning is like trying to erase memories from a trained AI model. Researchers have been working on ways to “forget” specific data that the model learned earlier. One approach, called Fine-tuning (FT), tries to make the model forget by updating its weights with new information. However, this method has some problems – it can’t really make the model forget what it learned before. This paper explores why that is and proposes a new way to help the model forget, called Retention-Based Masking (RBM). The authors show that RBM is better than other methods at forgetting what the model learned earlier while still keeping the important parts of its memory. |
Keywords
» Artificial intelligence » Fine tuning » Linear regression
