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Machine Unlearning

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Showing 351–375 of 438 papers

TitleStatusHype
Silver Linings in the Shadows: Harnessing Membership Inference for Machine Unlearning—0
Single Image Unlearning: Efficient Machine Unlearning in Multimodal Large Language Models—0
A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning—0
Sky of Unlearning (SoUL): Rewiring Federated Machine Unlearning via Selective Pruning—0
ViT-MUL: A Baseline Study on Recent Machine Unlearning Methods Applied to Vision Transformers—0
A Unified Framework for Continual Learning and Unlearning—0
Soft Token Attacks Cannot Reliably Audit Unlearning in Large Language Models—0
Soft Weighted Machine Unlearning—0
A Survey on Federated Unlearning: Challenges and Opportunities—0
SoK: Machine Unlearning for Large Language Models—0
Speech Unlearning—0
Split, Unlearn, Merge: Leveraging Data Attributes for More Effective Unlearning in LLMs—0
Prompt Attacks Reveal Superficial Knowledge Removal in Unlearning Methods—0
Survey of Security and Data Attacks on Machine Unlearning In Financial and E-Commerce—0
TAIJI: MCP-based Multi-Modal Data Analytics on Data Lakes—0
Attribute-to-Delete: Machine Unlearning via Datamodel Matching—0
TAPE: Tailored Posterior Difference for Auditing of Machine Unlearning—0
Targeted Unlearning Using Perturbed Sign Gradient Methods With Applications On Medical Images—0
Attack and Reset for Unlearning: Exploiting Adversarial Noise toward Machine Unlearning through Parameter Re-initialization—0
A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions—0
Textual Unlearning Gives a False Sense of Unlearning—0
The Frontier of Data Erasure: Machine Unlearning for Large Language Models—0
The Illusion of Unlearning: The Unstable Nature of Machine Unlearning in Text-to-Image Diffusion Models—0
What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests—0
The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining—0
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