Existing Literature about Machine Unlearning
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Updated
May 2, 2024
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. arXiv:2307.09218.
[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)
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.
Code for CVPR22 paper "Deep Unlearning via Randomized Conditionally Independent Hessians"
T̶̘̊h̷̙͘į̸̀ș̷͌ ̴̳̀r̴̬̕e̷̬͐p̵͍̚o̵̧̎s̶̗͂i̷͚̿t̷̟͝õ̴͙ř̵̘y̷̛̪ ̴̮͌i̶͊͜s̴̠̊ ̴̼͗f̶͘͜i̵̮͊n̴̨̊e̶̖̍!̷̝͋ ̴̨͛T̷̐͜h̷̺̔e̶̩̍r̸̰͒é̶̥ ̸̻̇ȉ̶͍s̵̡̍ ̴̛̫n̶̼̓ọ̷̀t̸̊ͅh̵̙͑ĩ̶͚n̵͙̋g̴̫̃ ̸̼͊w̷̘̿r̶̩̓o̷̠͝n̷͉͌g̶̞͒ ̷̛̼ọ̸̓v̶͍̈́e̵̺͑r̸̻̄ ̴̲̀h̸̉ͅé̶͙r̷̻̾e̷̠͛.̸̨̌
Continual Forgetting for Pre-trained Vision Models (CVPR 2024)
A federated clustering approach with the corresponding unlearning mechanism (ICLR 2023)
A resource repository for machine unlearning in large language models
Certified (approximate) machine unlearning for simplified graph convolutional networks (SGCs) with theoretical guarantees (ICLR 2023)
Machine Unlearning for Random Forests
An Empirical Study of Federated Unlearning: Efficiency and Effectiveness (Accepted Conference Track Papers at ACML 2023)
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