Aldaghri et al. |
Coded Machine Unlearning |
IEEE Access |
Brophy and Lowd |
Machine Unlearning for Random Forests |
ICML |
Bourtoule et al. |
Machine Unlearning |
S&P |
Chen et al. |
When Machine Unlearning Jeopardizes Privacy |
CCS |
Dang et al. |
Right to Be Forgotten in the Age of Machine Learning |
ICADS |
Golatkar et al. |
Mixed-Privacy Forgetting in Deep Networks |
CVPR |
Goyal et al. |
Revisiting Machine Learning Training Process for Enhanced Data Privacy |
IC3 |
Graves et al. |
Amnesiac Machine Learning |
AAAI |
Gupta et al. |
Adaptive Machine Unlearning |
Neurips |
Huang et al. |
Unlearnable Examples: Making Personal Data Unexploitable |
ICLR |
Huang et al. |
EMA: Auditing Data Removal from Trained Models |
MICCAI |
Khan and Swaroop |
Knowledge-Adaptation Priors |
NeurIPS |
Liu et al. |
FedEraser: Enabling Efficient Client-Level Data Removal from Federated Learning Models |
IWQoS |
Liu et al. |
RevFRF: Enabling Cross-domain Random Forest Training with Revocable Federated Learning |
IEEE Trans. Secure Dep. Comp. |
Neel et al. |
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning |
ALT |
Schelter et al. |
HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning |
SIGMOD |
Sekhari et al. |
Remember What You Want to Forget: Algorithms for Machine Unlearning |
Neurips |
Shibata et al. |
Learning with Selective Forgetting |
IJCAI |
Jose and Simeone |
A Unified PAC-Bayesian Framework for Machine Unlearning via Information Risk Minimization |
MLSP Workshop |
Tahiliani et al. |
Machine Unlearning: Its Need and Implementation Strategies |
IC3 |
Ullah et al. |
Machine Unlearning via Algorithmic Stability |
COLT |
Wang and Schelter |
Efficiently Maintaining Next Basket Recommendations under Additions and Deletions of Baskets and Items |
ORSUM Workshop |
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Chen et al. |
Graph Unlearning |
arXiv |
Chen et al. |
Machine unlearning via GAN |
arXiv |
He et al. |
DeepObliviate: A Powerful Charm for Erasing Data Residual Memory in Deep Neural Networks |
arXiv |
Madahaven and Mathioudakis |
Certifiable Machine Unlearning for Linear Models |
arXiv |
Parne et al. |
Machine Unlearning: Learning, Polluting, and Unlearning for Spam Email |
arXiv |
Peste et al. |
SSSE: Efficiently Erasing Samples from Trained Machine Learning Models |
arXiv |
Tarun et al. |
Fast Yet Effective Machine Unlearning |
arXix |
Thudi et al. |
Unrolling SGD: Understanding Factors Influencing Machine Unlearning |
arXiv |
Thudi et al. |
On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning |
arXiv |
Thudi et al. |
Bounding Membership Inference |
arXiv |
Wang et al. |
Federated Unlearning via Class-Discriminative Pruning |
arXiv |
Warnecke et al. |
Machine Unlearning for Features and Labels |
arXiv |
Zeng et al. |
Learning to Refit for Convex Learning Problems |
arXiv |