Existing Literature about Machine Unlearning
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Updated
Jan 11, 2025
Existing Literature about Machine Unlearning
Awesome Machine Unlearning (A Survey of Machine Unlearning)
A curated list of trustworthy deep learning papers. Daily updating...
A resource repository for machine unlearning in large language models
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. TPAMI, 2024.
[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
[ACL 2024] Code and data for "Machine Unlearning of Pre-trained Large Language Models"
Official implementation of "When Machine Unlearning Jeopardizes Privacy" (ACM CCS 2021)
Continual Forgetting for Pre-trained Vision Models (CVPR 2024)
Official implementation of "Graph Unlearning" (ACM CCS 2022)
General Strategy for Unlearning in Graph Neural Networks
A notebook of awesome privacy protection,federated learning, fairness and blockchain research materials.
A Visual Analytics System for Comparative Evaluation of Machine Unlearning Methods
Code for CVPR22 paper "Deep Unlearning via Randomized Conditionally Independent Hessians"
"Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning" by Chongyu Fan*, Jiancheng Liu*, Licong Lin*, Jinghan Jia, Ruiqi Zhang, Song Mei, Sijia Liu
A federated clustering approach with the corresponding unlearning mechanism (ICLR 2023)
T̶̘̊h̷̙͘į̸̀ș̷͌ ̴̳̀r̴̬̕e̷̬͐p̵͍̚o̵̧̎s̶̗͂i̷͚̿t̷̟͝õ̴͙ř̵̘y̷̛̪ ̴̮͌i̶͊͜s̴̠̊ ̴̼͗f̶͘͜i̵̮͊n̴̨̊e̶̖̍!̷̝͋ ̴̨͛T̷̐͜h̷̺̔e̶̩̍r̸̰͒é̶̥ ̸̻̇ȉ̶͍s̵̡̍ ̴̛̫n̶̼̓ọ̷̀t̸̊ͅh̵̙͑ĩ̶͚n̵͙̋g̴̫̃ ̸̼͊w̷̘̿r̶̩̓o̷̠͝n̷͉͌g̶̞͒ ̷̛̼ọ̸̓v̶͍̈́e̵̺͑r̸̻̄ ̴̲̀h̸̉ͅé̶͙r̷̻̾e̷̠͛.̸̨̌
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