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


  1. (really good) A survey of MU.

  2. (really good) Awesome MU on Github (website)- 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.

  1. Who's Harry Potter? Approximate Unlearning in LLMs. Arxiv, paper, Microsoft, medium

  2. A review on MU, Zhang et al.


  1. A fresh perspective on machine unlearning, with a real-world solution! 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, 2


  1. This repository contains the core code used in the SISA experiments of our Machine Unlearning paper along with some example scripts.

  2. This repository contains the code used in our experiments of our paper on Evaluating Machine Unlearning in the src/ folder along with some sample scripts in the scripts/ folder.

  3. This is a Python implementation of "Towards Unbounded Machine Unlearning"


  1. NeurIPS 2023 Kaggle - Machine Unlearning Erase the influence of requested samples without hurting accuracy

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