| Marchant et al. |
Hard to Forget: Poisoning Attacks on Certified Machine Unlearning |
AAAI |
| Wu et al. |
PUMA: Performance Unchanged Model Augmentation for Training Data Removal |
AAAI |
| Nguyen et al. |
Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten |
ASIA CCS |
| Mehta et al. |
Deep Unlearning via Randomized Conditionally Independent Hessians |
CVPR |
| Ye et al. |
Learning with Recoverable Forgetting |
ECCV |
| Thudi et al. |
Unrolling SGD: Understanding Factors Influencing Machine Unlearning |
EuroS&P |
| Fu et al. |
Knowledge Removal in Sampling-based Bayesian Inference |
ICLR |
| Hu et al. |
Membership Inference via Backdooring |
IJCAI |
| Yan et al. |
ARCANE: An Efficient Architecture for Exact Machine Unlearning |
IJCAI |
| Liu et al. |
The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining |
INFOCOM |
| Liu et al. |
Backdoor Defense with Machine Unlearning |
INFOCOM |
| Gao et al. |
Deletion Inference, Reconstruction, and Compliance in Machine (Un)Learning |
PETS |
| Sommer et al. |
Athena: Probabilistic Verification of Machine Unlearning |
PoPETs |
| Ganhor et al. |
Unlearning Protected User Attributes in Recommendations with Adversarial Training |
SIGIR |
| Chen et al. |
Recommendation Unlearning |
TheWebConf |
| Thudi et al. |
On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning |
USENIX Security |
| Wang et al. |
Federated Unlearning via Class-Discriminative Pruning |
WWW |
|
|
|
| Ma et al. |
Learn to Forget: Machine Unlearning Via Neuron Masking |
IEEE Trans. Secure Dep. Comp. |
| Lu et al. |
Label-only membership inference attacks on machine unlearning without dependence of posteriors |
Int. J. Intel. Systems |
| Meng et al. |
Active forgetting via influence estimation for neural networks |
Int. J. Intel. Systems |
| Baumhauer et al. |
Machine Unlearning: Linear Filtration for Logit-based Classifiers |
Machine Learning |
| Mahadaven and Mathiodakis |
Certifiable Unlearning Pipelines for Logistic Regression: An Experimental Study |
Machine Learning and Knowledge Extraction |
|
|
|
| Kim and Woo |
Efficient Two-Stage Model Retraining for Machine Unlearning |
CVPR Workshop |
| Yoon et al. |
Few-Shot Unlearning |
SRML Workshop |
| Halimi et al. |
Federated Unlearning: How to Efficiently Erase a Client in FL? |
UpML Workshop |
| Rawat et al. |
Challenges and Pitfalls of Bayesian Unlearning |
UpML Workshop |
|
|
|
| Carlini et al. |
The Privacy Onion Effect: Memorization is Relative |
arXiv |
| Chien et al. |
Certified Graph Unlearning |
arXiv |
| Chilkuri et al. |
Debugging using Orthogonal Gradient Descent |
arXiv |
| Chundawat et al. |
Zero-Shot Machine Unlearning |
arXiv |
| Chundawat et al. |
Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teacher |
arXiv |
| Dai et al. |
Knowledge Neurons in Pretrained Transformers |
arXiv |
| Gao et al. |
VeriFi: Towards Verifiable Federated Unlearning |
arXiv |
| Goel et al. |
Evaluating Inexact Unlearning Requires Revisiting Forgetting |
arXiv |
| Guo et al. |
Vertical Machine Unlearning: Selectively Removing Sensitive Information From Latent Feature Space |
arXiv |
| Guo et al. |
Efficient Attribute Unlearning: Towards Selective Removal of Input Attributes from Feature Representations |
arXiv |
| Liu et al. |
Continual Learning and Private Unlearning |
arXiv |
| Liu et al. |
Forgetting Fast in Recommender Systems |
arXiv |
| Tarun et al. |
Fast Yet Effective Machine Unlearning |
arXiv |
| Wu et al. |
Federated Unlearning with Knowledge Distillation |
arXiv |
| Yoon et al. |
Few-Shot Unlearning by Model Inversion |
arXiv |
| Cong and Mahdavi |
Privacy Matters! Efficient Graph Representation Unlearning with Data Removal Guarantee |
|
| Cong and Mahdavi |
GraphEditor: An Efficient Graph Representation Learning and Unlearning Approach |
|
| Tanno et al. |
Repairing Neural Networks by Leaving the Right Past Behind |
|
| Wu et al. |
Provenance-based Model Maintenance: Implications for Privacy |
|
