Existing Literature about Machine Unlearning

Awesome Machine Unlearning (A Survey of Machine Unlearning)

A curated list of trustworthy deep learning papers. Daily updating...

A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. TPAMI, 2024.

A resource repository for machine unlearning in large language models

[ICLR24 (Spotlight)] "SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation" by Chongyu Fan*, Jiancheng Liu*, Yihua Zhang, Eric Wong, Dennis Wei, Sijia Liu

Awesome Federated Unlearning (FU) Papers (Continually Update)

[NeurIPS23 (Spotlight)] "Model Sparsity Can Simplify Machine Unlearning" by Jinghan Jia*, Jiancheng Liu*, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, Sijia Liu

Official implementation of "When Machine Unlearning Jeopardizes Privacy" (ACM CCS 2021)

[ACL 2024] Code and data for "Machine Unlearning of Pre-trained Large Language Models"

General Strategy for Unlearning in Graph Neural Networks

Official implementation of "Graph Unlearning" (ACM CCS 2022)

Continual Forgetting for Pre-trained Vision Models (CVPR 2024)

A notebook of awesome privacy protection,federated learning, fairness and blockchain research materials.

Code for CVPR22 paper "Deep Unlearning via Randomized Conditionally Independent Hessians"

T̶̘̊h̷̙͘į̸̀ș̷͌ ̴̳̀r̴̬̕e̷̬͐p̵͍̚o̵̧̎s̶̗͂i̷͚̿t̷̟͝õ̴͙ř̵̘y̷̛̪ ̴̮͌i̶͊͜s̴̠̊ ̴̼͗f̶͘͜i̵̮͊n̴̨̊e̶̖̍!̷̝͋ ̴̨͛T̷̐͜h̷̺̔e̶̩̍r̸̰͒é̶̥ ̸̻̇ȉ̶͍s̵̡̍ ̴̛̫n̶̼̓ọ̷̀t̸̊ͅh̵̙͑ĩ̶͚n̵͙̋g̴̫̃ ̸̼͊w̷̘̿r̶̩̓o̷̠͝n̷͉͌g̶̞͒ ̷̛̼ọ̸̓v̶͍̈́e̵̺͑r̸̻̄ ̴̲̀h̸̉ͅé̶͙r̷̻̾e̷̠͛.̸̨̌

Certified (approximate) machine unlearning for simplified graph convolutional networks (SGCs) with theoretical guarantees (ICLR 2023)

A federated clustering approach with the corresponding unlearning mechanism (ICLR 2023)

"Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning" by Chongyu Fan*, Jiancheng Liu*, Licong Lin*, Jinghan Jia, Ruiqi Zhang, Song Mei, Sijia Liu