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Machine Unlearning

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TitleStatusHype
τ: Gradient-based and Task-Agnostic machine Unlearning—0
Threats, Attacks, and Defenses in Machine Unlearning: A Survey—0
Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Approximate Unlearning Completeness—0
Second-Order Information Matters: Revisiting Machine Unlearning for Large Language Models—0
SAP: Corrective Machine Unlearning with Scaled Activation Projection for Label Noise RobustnessCode0
Federated Knowledge Graph Unlearning via Diffusion Model—0
Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects—0
Towards Independence Criterion in Machine Unlearning of Features and Labels—0
Efficient Knowledge Deletion from Trained Models through Layer-wise Partial Machine Unlearning—0
Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine UnlearningCode1
A Survey on Federated Unlearning: Challenges and Opportunities—0
Dissecting Language Models: Machine Unlearning via Selective PruningCode0
Inexact Unlearning Needs More Careful Evaluations to Avoid a False Sense of PrivacyCode0
Loss-Free Machine Unlearning—0
Eight Methods to Evaluate Robust Unlearning in LLMs—0
Machine Unlearning of Pre-trained Large Language ModelsCode2
Remaining-data-free Machine Unlearning by Suppressing Sample Contribution—0
Corrective Machine UnlearningCode1
UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion ModelsCode2
Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning—0
Towards Safer Large Language Models through Machine UnlearningCode1
Rethinking Machine Unlearning for Large Language Models—0
Discriminative Adversarial Unlearning—0
Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models—0
ViT-MUL: A Baseline Study on Recent Machine Unlearning Methods Applied to Vision Transformers—0
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