| 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 |
| Ye and Lu |
Sequence Unlearning for Sequential Recommender Systems |
AI |
| Wang et al. |
BFU: Bayesian Federated Unlearning with Parameter Self-Sharing |
Asia CCS |
| Lee and Woo |
UNDO: Effective and Accurate Unlearning Method for Deep Neural Networks |
CIKM |
| 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 |
| Mireshghallah et al. |
Simple Temporal Adaptation to Changing Label Sets: Hashtag Prediction via Dense KNN |
EMNLP |
| Gandikota et al. |
Erasing Concepts from Diffusion Models |
ICCV |
| Liu et al. |
MUter: Machine Unlearning on Adversarially Trained Models |
ICCV |
| Zheng et al. |
Graph Unlearning Using Knowledge Distillation |
ICICS |
| 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 |
| Liu et al. |
Machine Unlearning with Affine Hyperplane Shifting and Maintaining for Image Classification |
ICONIP |
| 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 |
| Li et al. |
Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems |
MM |
| 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 |
| Chen et al. |
Fast Model Debias with Machine Unlearning |
NeurIPS |
| Kurmanji et al. |
Towards Unbounded 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 |
| Heng et al. |
Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models |
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 |
| Kurmanji et al. |
Machine Unlearning in Learned Databases: An Experimental Analysis |
SIGMOD |
| Wu et al. |
DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning |
SIGMOD |
| Wang et al. |
Inductive Graph Unlearning |
USENIX Security |
| Xia et al. |
Equitable Data Valuation Meets the Right to Be Forgotten in Model Markets |
VLDB |
| 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 |
| Sai et al. |
Machine Un-learning: An Overview of Techniques, Applications, and Future Directions |
Cognitive Computation |
| Tang et al. |
Ensuring User Privacy and Model Security via Machine Unlearning: A Review |
Computers, Materials, and Continua |
| 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 |
| Zhao et al. |
Federated Unlearning With Momentum Degradation |
IEEE IOT Journal |
| 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 |
| Wang et al. |
Machine Unlearning via Representation Forgetting with Parameter Self-Sharing |
IEEE Trans. Info. Forensics and Security |
| Guo et al. |
Verifying in the Dark: Verifiable Machine Unlearning by Using Invisible Backdoor Triggers |
IEEE Trans. Info. Forensics and Security |
| Zhang et al. |
FedRecovery: Differentially Private Machine Unlearning for Federated Learning Frameworks |
IEEE Trans. Info. Forensics and Security |
| Guo et al. |
FAST: Adopting Federated Unlearning to Eliminating Malicious Terminals at Server Side |
IEEE Trans. Network Science and Engineering |
| Tarun et al. |
Fast Yet Effective Machine Unlearning |
IEEE Trans. Neural Net. and Learn. Systems |
| Tang et al. |
Fuzzy rough unlearning model for feature selection |
International Journal of Approximate Reasoning |
| Zhu et al. |
Hierarchical Machine Unlearning |
Learning and Intelligent Optimization |
| 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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|
| Kodge et al. |
Deep Unlearning: Fast and Efficient Training Free Approach to Controlled Forgetting |
arXiv |
| Abbasi et al. |
Brainwash: A Poisoning Attack to Forget in Continual Learning |
arXiv |
| Abbasi et al. |
CovarNav: Machine Unlearning via Model Inversion and Covariance Navigation |
arXiv |
| Alam et al. |
Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning |
arXiv |
| Bother et al. |
Modyn: A Platform for Model Training on Dynamic Datasets With Sample-Level Data Selection |
arXiv |
| Cai et al. |
Where have you been? A Study of Privacy Risk for Point-of-Interest Recommendation |
arXiv |
| Cha et al. |
Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers |
arXiv |
| Chen et al. |
Unlearn What You Want to Forget: Efficient Unlearning for LLMs |
arXiv |
| Cheng et al. |
Multimodal Machine Unlearning |
arXiv |
| Cong and Mahdavi |
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection |
arXiv |
| Cotogni et al. |
DUCK: Distance-based Unlearning via Centroid Kinematics |
arXiv |
| Dam et al. |
Delete My Account: Impact of Data Deletion on Machine Learning Classifiers |
arXiv |
| Dhasade et al. |
QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillation |
arXiv |
| Dukler et al. |
SAFE: Machine Unlearning With Shard Graphs |
arXiv |
| Eldan et al. |
Who’s Harry Potter? Approximate Unlearning in LLMs |
arXiv |
| Fan et al. |
SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation |
arXiv |
| Foster et al. |
Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening |
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 |
| Kim et al. |
Layer Attack Unlearning: Fast and Accurate Machine Unlearning via Layer Level Attack and Knowledge Distillation |
arXiv |
| Kodge et al. |
Deep Unlearning: Fast and Efficient Training-free Approach to Controlled Forgetting |
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 |
| Liu et al. |
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning |
arXiv |
| Liu et al. |
A Survey on Federated Unlearning: Challenges, Methods, and Future Directions |
arXiv |
| Moon et al. |
Feature Unlearning for Generative Models via Implicit Feedback |
arXiv |
| Panda and AP |
FAST: Feature Aware Similarity Thresholding for Weak Unlearning in Black-Box Generative Models |
arXiv |
| Pawelczyk et al. |
In-Context Unlearning: Language Models As Few Shot Unlearners |
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 |
| Shah et al. |
Unlearning via Sparse Representations |
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 |
| Shi et al. |
DeepClean: Machine Unlearning on the Cheap by Resetting Privacy Sensitive Weights using the Fisher Diagonal |
arXiv |
| Shi et al. |
Detecting Pretraining Data from Large Language Models |
arXiv |
| Si et al. |
Knowledge Unlearning for LLMs: Tasks, Methods, and Challenges |
arXiv |
| Sinha et al. |
Distill to Delete: Unlearning in Graph Networks with Knowledge Distillation |
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 |
| Tian et al. |
DeRDaVa: Deletion-Robust Data Valuation for Machine Learning |
arXiv |
| Tiwary et al. |
Adapt then Unlearn: Exploiting Parameter Space Semantics for Unlearning in Generative Adversarial Networks |
arXiv |
| Wu et al. |
DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models |
arXiv |
| Xin et al. |
On the Effectiveness of Unlearning in Session-Based Recommendation |
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 |
| Ye et al. |
Reinforcement Unlearning |
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 |
| Zhang et al. |
To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images … For Now |
arXiv |
| Zhang et al. |
SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning |
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 |
|
| Su and Li |
Asynchronous Federated Unlearning |
|
| Xiong et al. |
Exact-Fun: An Exact and Efficient Federated Unlearning Approach |
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