Wang et al. |
KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment |
ACL |
Yu et al. |
Unlearning Bias in Language Models by Partitioning Gradients |
ACL |
Zhang et al. |
Machine Unlearning Methodology base on Stochastic Teacher Network |
ADMA |
Wang et al. |
BFU: Bayesian Federated Unlearning with Parameter Self-Sharing |
Asia CCS |
Ghazi et al. |
Ticketed Learning-Unlearning Schemes |
COLT |
Chen et al. |
Boundary Unlearning: Rapid Forgetting of Deep Networks via Shifting the Decision Boundary |
CVPR |
Lin et al. |
ERM-KTP: Knowledge-Level Machine Unlearning via Knowledge Transfer |
CVPR |
Hagos et al. |
Unlearning Spurious Correlations in Chest X-ray Classification |
Discovery Science |
Lin et al. |
Machine Unlearning in Gradient Boosting Decision Trees |
KDD |
Qian et al. |
Towards Understanding and Enhancing Robustness of Deep Learning Models against Malicious Unlearning Attacks |
KDD |
Wu et al. |
Certified Edge Unlearning for Graph Neural Networks |
KDD |
Liu et al. |
MUter: Machine Unlearning on Adversarially Trained Models |
ICCV |
Cheng et al. |
GNNDelete: A General Strategy for Unlearning in Graph Neural Networks |
ICLR |
Chien et al. |
Efficient Model Updates for Approximate Unlearning of Graph-Structured Data |
ICLR |
Che et al. |
Fast Federated Machine Unlearning with Nonlinear Functional Theory |
ICML |
Hu et al. |
A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services |
NDSS |
Warnecke et al. |
Machine Unlearning for Features and Labels |
NDSS |
Kurmanji et al. |
Towards Unbounded Machine Unlearning |
NeurIPS |
Chen et al. |
Test-time Model DeBias Via Machine Unlearning |
NeurIPS |
Li et al. |
UltraRE: Enhancing RecEraser for Recommendation Unlearning via Error Decomposition |
NeurIPS |
Liu et al. |
Certified Minimax Unlearning with Generalization Rates and Deletion Capacity |
NeurIPS |
Jia et al. |
Model Sparsification Can Simplify Machine Unlearning |
NeurIPS |
Wei et al. |
Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examples |
NeurIPS |
Di et al. |
Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks |
NeurIPS |
Leysen |
Exploring Unlearning Methods to Ensure the Privacy, Security, and Usability of Recommender Systems |
RecSys |
Koch and Soll |
No Matter How You Slice It: Machine Unlearning with SISA Comes at the Expense of Minority Classes |
SaTML |
Schelter et al. |
Forget Me Now: Fast and Exact Unlearning in Neighborhood-based Recommendation |
SIGIR |
Wu et al. |
DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning |
SIGMOD |
Wang et al. |
Inductive Graph Unlearning |
USENIX Security |
Sun et al. |
Lazy Machine Unlearning Strategy for Random Forests |
WISA |
Pan et al. |
Unlearning Graph Classifiers with Limited Data Resources |
WWW |
Wu et al. |
GIF: A General Graph Unlearning Strategy via Influence Function |
WWW |
Zhu et al. |
Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning |
WWW |
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Chen et al. |
Privacy preserving machine unlearning for smart cities |
Annals of Telemcommunications |
Zhang et al. |
Machine Unlearning by Reversing the Continual Learning |
Applied Sciences |
Deng et al. |
Vertical Federated Unlearning on the Logistic Regression Model |
Electronics |
Zeng at al. |
Towards Highly-efficient and Accurate Services QoS Prediction via Machine Unlearning |
IEEE Access |
Xia et al. |
FedME2: Memory Evaluation & Erase Promoting Federated Unlearning in DTMN |
IEEE Selected Areas in Communications |
Zhang et al. |
Poison Neural Network-Based mmWave Beam Selection and Detoxification With Machine Unlearning |
IEEE Trans. on Comm. |
Chundawat et al. |
Zero-Shot Machine Unlearning |
IEEE Trans. Info. Forensics and Security |
Zhang et al. |
FedRecovery: Differentially Private Machine Unlearning for Federated Learning Frameworks |
IEEE Trans. Info. Forensics and Security |
Tarun et al. |
Fast Yet Effective Machine Unlearning |
IEEE Trans. Neural Net. and Learn. Systems |
Floridi |
Machine Unlearning: its nature, scope, and importance for a “delete culture” |
Philosophy & Technology |
Zhang et al. |
A Review on Machine Unlearning |
SN Computer Science |
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Oesterling et al. |
Fair Machine Unlearning: Data Removal while Mitigating Disparities |
DMLR Workshop |
Llamas et al. |
Effective Machine Learning-based Access Control Administration through Unlearning |
EuroS&PW |
Bae et al. |
Gradient Surgery for One-shot Unlearning on Generative Model |
Generative AI & LAW Workshop |
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Alam et al. |
Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning |
arXiv |
Cha et al. |
Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers |
arXiv |
Cong and Mahdavi |
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection |
arXiv |
Dukler et al. |
SAFE: Machine Unlearning With Shard Graphs |
arXiv |
Foster et al. |
Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening |
arXiv |
Gandikota et al. |
Erasing Concepts from Diffusion Models |
arXiv |
Huang et al. |
Tight Bounds for Machine Unlearning via Differential Privacy |
arXiv |
Jin et al. |
Forgettable Federated Linear Learning with Certified Data Removal |
arXiv |
Koh et al. |
Disposable Transfer Learning for Selective Source Task Unlearning |
arXiv |
Krishna et al. |
Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten |
arXiv |
LeBlond et al. |
Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile |
arXiv |
Li and Ghosh |
Random Relabeling for Efficient Machine Unlearning |
arXiv |
Li et al. |
Subspace based Federated Unlearning |
arXiv |
Li et al. |
Selective and Collaborative Influence Function for Efficient Recommendation Unlearning |
arXiv |
Li et al. |
Make Text Unlearnable: Exploiting Effective Patterns to Protect Personal Data |
arXiv |
Li et al. |
Federated Unlearning via Active Forgetting |
arXiv |
Liu et al. |
Tangent Transformers for Composition, Privacy and Removal |
arXiv |
Liu et al. |
Recommendation Unlearning via Matrix Correction |
arXiv |
Moon et al. |
Feature Unlearning for Generative Models via Implicit Feedback |
arXiv |
Poppi et al. |
Multi-Class Explainable Unlearning for Image Classification via Weight Filtering |
arXiv |
Qu et al. |
Learn to Unlearn: A Survey on Machine Unlearning |
arXiv |
Ramachandra and Sethi |
Machine Unlearning for Causal Inference |
arXiv |
Shaik et al. |
Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy |
arXiv |
Shaik et al. |
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning |
arXiv |
Sun et al. |
Generative Adversarial Networks Unlearning |
arXiv |
Tan et al. |
Unfolded Self-Reconstruction LSH: Towards Machine Unlearning in Approximate Nearest Neighbour Search |
arXiv |
Tiwary et al. |
Adapt then Unlearn: Exploiting Parameter Space Semantics for Unlearning in Generative Adversarial Networks |
arXiv |
Xu et al. |
Netflix and Forget: Efficient and Exact Machine Unlearning from Bi-linear Recommendations |
arXiv |
Xu and Teng |
Task-Aware Machine Unlearning and Its Application in Load Forecasting |
arXiv |
Yamashita et al. |
One-Shot Machine Unlearning with Mnemonic Code |
arXiv |
Zha et al. |
To Be Forgotten or To Be Fair: Unveiling Fairness Implications of Machine Unlearning Methods |
arXiv |
Zhang et al. |
Recommendation Unlearning via Influence Function |
arXiv |
Zhang et al. |
Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutions |
arXiv |
Zhou et al. |
Audit to Forget: A Unified Method to Revoke Patients' Private Data in Intelligent Healthcare |
bioRxiv |
Jahanian et al. |
Protecting the Neural Networks against FGSM Attack Using Machine Unlearning |
Research Square |
Fan |
Machine learning and unlearning for IoT anomaly detection |
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Su and Li |
Asynchronous Federated Unlearning |
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Xiong et al. |
Exact-Fun: An Exact and Efficient Federated Unlearning Approach |
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