A resource repository for machine unlearning in large language models
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Updated
May 28, 2024
A resource repository for machine unlearning in large language models
A curated list of trustworthy deep learning papers. Daily updating...
Existing Literature about Machine Unlearning
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 Machine Unlearning (A Survey of Machine Unlearning)
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
Machine Unlearning for Random Forests
This project explores the efficacy of machine unlearning methods like Task-Agnostic Machine Unlearning and SISA in enhancing privacy and reducing bias in facial recognition systems, emphasizing their importance in responsible technology implementation.
Continual Forgetting for Pre-trained Vision Models (CVPR 2024)
"Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning" by Chongyu Fan*, Jiancheng Liu*, Alfred Hero, Sijia Liu
A framework for machine unlearning.
Official Website of https://github.com/tamlhp/awesome-machine-unlearning
Awesome Federated Unlearning (FU) Papers (Continually Update)
Code for the paper "DUCK: Distance-based Unlearning via Centroid Kinematics"
This repo contains data and code for Task-Aware Machine Unlearning with Application to Load Forecasting.
[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
A curated list of Machine Unlearning, focusing on deep learning applications.
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