A curated list of Machine Unlearning, focusing on deep learning applications.
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Updated
Jan 4, 2024
A curated list of Machine Unlearning, focusing on deep learning applications.
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.
Exploring machine unlearning: from classification models to generative models like stable diffusion or LLMs. Inspired by 'Yesterday', it delves into removing learned data from AI models, aiming to preserve performance amid privacy and GDPR compliance challenges.
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
A framework for machine unlearning.
USENIX Security'23: Inductive Graph Unlearning
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
Code for the paper "DUCK: Distance-based Unlearning via Centroid Kinematics"
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.
Unlearning Graph Classifiers with Limited Data Resources (TheWebConf 2023)
"Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning" by Chongyu Fan*, Jiancheng Liu*, Alfred Hero, Sijia Liu
Official PyTorch Implementation for Continual Learning and Private Unlearning
An Empirical Study of Federated Unlearning: Efficiency and Effectiveness (Accepted Conference Track Papers at ACML 2023)
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)
Machine Unlearning for Random Forests
T̶̘̊h̷̙͘į̸̀ș̷͌ ̴̳̀r̴̬̕e̷̬͐p̵͍̚o̵̧̎s̶̗͂i̷͚̿t̷̟͝õ̴͙ř̵̘y̷̛̪ ̴̮͌i̶͊͜s̴̠̊ ̴̼͗f̶͘͜i̵̮͊n̴̨̊e̶̖̍!̷̝͋ ̴̨͛T̷̐͜h̷̺̔e̶̩̍r̸̰͒é̶̥ ̸̻̇ȉ̶͍s̵̡̍ ̴̛̫n̶̼̓ọ̷̀t̸̊ͅh̵̙͑ĩ̶͚n̵͙̋g̴̫̃ ̸̼͊w̷̘̿r̶̩̓o̷̠͝n̷͉͌g̶̞͒ ̷̛̼ọ̸̓v̶͍̈́e̵̺͑r̸̻̄ ̴̲̀h̸̉ͅé̶͙r̷̻̾e̷̠͛.̸̨̌
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