Existing Literature about Machine Unlearning
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Updated
Jan 26, 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
Continual Forgetting for Pre-trained Vision Models (CVPR 2024)
[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)
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.
Visual Analytics for Bridging Unlearning and Retrained Models
Code for CVPR22 paper "Deep Unlearning via Randomized Conditionally Independent Hessians"
[ECCV24] "Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning" by Chongyu Fan*, Jiancheng Liu*, Alfred Hero, Sijia Liu
[NeurIPS 2024] Large Language Model Unlearning via Embedding-Corrupted Prompts
"Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning" by Chongyu Fan*, Jiancheng Liu*, Licong Lin*, Jinghan Jia, Ruiqi Zhang, Song Mei, Sijia Liu
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