There are 6 repositories under machine-unlearning topic.
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.
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)
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.̸̨̌
Machine Unlearning for Random Forests
A federated clustering approach with the corresponding unlearning mechanism (ICLR 2023)
Certified (approximate) machine unlearning for simplified graph convolutional networks (SGCs) with theoretical guarantees (ICLR 2023)
An Empirical Study of Federated Unlearning: Efficiency and Effectiveness (Accepted Conference Track Papers at ACML 2023)
Official PyTorch Implementation for Continual Learning and Private Unlearning
"Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning" by Chongyu Fan*, Jiancheng Liu*, Alfred Hero, Sijia Liu
Unlearning Graph Classifiers with Limited Data Resources (TheWebConf 2023)
Code for Large Language Model Unlearning via Embedding-Corrupted Prompts
An implementation of the SIGMOD24 paper: Machine Unlearning in Learned DBs: An Experimental Analysis
This repo contains data and code for Task-Aware Machine Unlearning with Application to Load Forecasting.
Official Website of https://github.com/tamlhp/awesome-machine-unlearning
# kaefa kwangwoon automated exploratory factor analysis for improving research capability to identify unexplained factor structure with complexly cross-classified multilevel structured data in R environment
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
Introduction to machine unlearning: from classification models to simple language models. Inspired by 'Yesterday', it delves into removing learned data from AI models, aiming to preserve performance amid privacy and GDPR compliance challenges.
A framework for machine unlearning.
We explore the ways to "Unlearn" (you read it right), certain concepts/things while retaining general ability of a generative text-to-image model (stable diffusion 2.1). This project is conducted as a term project for Learning from data class at ITU (Istanbul Technical University)
This repository is an implementation of machine unlearning in deep learning model.