# Unlearning

Machine unlearning (MU) refers to the challenge of erasing a data point's influence on the input-output mapping of an ML model.

## Papers

1. (really good) A [survey](https://arxiv.org/abs/2209.02299) of [MU](https://arxiv.org/pdf/2209.02299.pdf).
2. [Existing literature on MU](https://github.com/jjbrophy47/machine_unlearning)
3. [Machine Unlearning: The Right to be Forgotten](https://www.kaggle.com/code/tamlhp/machine-unlearning-the-right-to-be-forgotten#sec:algorithms)
4. (really good) [Awesome MU on Github](https://github.com/tamlhp/awesome-machine-unlearning?tab=readme-ov-file#type-image) ([website](https://awesome-machine-unlearning.github.io/))- a collection of academic articles, published methodology, and datasets on the subject of machine unlearning. model agnostic, intrinsic, and data-driven approaches, evaluation metrics, and datasets.

<figure><img src="https://83674056-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mgd48oS5_duTKOVE_Et%2Fuploads%2FUeWSyZzmQpZAr3Zk6d4n%2Fimage.png?alt=media&#x26;token=0755c8ca-544b-4ff8-a9d9-1099473c43bc" alt=""><figcaption></figcaption></figure>

5. Who's Harry Potter? Approximate Unlearning in LLMs. [Arxiv](https://arxiv.org/abs/2310.02238), [paper](https://browse.arxiv.org/pdf/2310.02238), [Microsoft](https://www.microsoft.com/en-us/research/project/physics-of-agi/articles/whos-harry-potter-making-llms-forget-2/), [medium](https://pub.towardsai.net/who-is-harry-potter-inside-microsoft-researchs-fine-tuning-method-for-unlearning-concepts-in-llms-33dfe8e742a9)&#x20;
6. [fast yet effective MU](https://arxiv.org/pdf/2111.08947.pdf)
7. [one shot MU](https://arxiv.org/pdf/2201.05629.pdf)
8. [A review on MU](https://link.springer.com/article/10.1007/s42979-023-01767-4), Zhang et al.
9. [Machine Un-learning: An Overview of Techniques, Applications, and Future Directions](https://link.springer.com/article/10.1007/s12559-023-10219-3), Siva et al.
10. [Machine Unlearning](https://arxiv.org/abs/1912.03817)

## Medium

1. [A fresh perspective on machine unlearning, with a real-world solution!](https://medium.com/@aliborji/a-fresh-perspective-on-machine-unlearning-with-a-real-world-solution-203821dd01c0) a solution that uses three of the following approaches. Data Augmentation, Weight Decay, Fine-Tuning, Selective Retraining, and Neural Architecture Modifications.
2. What is MU? Part [1](https://medium.com/@choquette.christopher/what-is-machine-unlearning-pt-1-933ff53dc9a6), [2](https://medium.com/@choquette.christopher/how-to-do-machine-unlearning-pt-2-ae32cb6ca2f1)

## GitHub

1. [search results](https://github.com/search?q=unlearning\&type=repositories)
2. This [repository](https://github.com/cleverhans-lab/machine-unlearning) contains the core code used in the SISA experiments of our [Machine Unlearning](https://arxiv.org/abs/1912.03817) paper along with some example scripts.
3. [Implementations of various data deletion methods.](https://github.com/ChrisWaites/data-deletion?tab=readme-ov-file) \
   [Evaluation Doc.](https://docs.google.com/document/d/14B_aLihLTNE7a2yRQHNRRVwvSOkttakVYFhlayZBNkE/edit)
4. This [repository](https://github.com/shash42/Evaluating-Inexact-Unlearning/tree/master) contains the code used in our experiments of our paper on [Evaluating Machine Unlearning](https://arxiv.org/abs/2201.06640) in the src/ folder along with some sample scripts in the scripts/ folder.
5. #### [This](https://github.com/meghdadk/SCRUB) is a Python implementation of "Towards Unbounded Machine Unlearning"
6. #### [data deletion](https://github.com/ChrisWaites/data-deletion?tab=readme-ov-file)

## Community

1. NeurIPS 2023 [Kaggle](https://www.kaggle.com/competitions/neurips-2023-machine-unlearning/leaderboard) - Machine Unlearning Erase the influence of requested samples without hurting accuracy