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Machine Unlearning

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Showing 201–250 of 438 papers

TitleStatusHype
Mitigating Backdoor Attacks using Activation-Guided Model Editing—0
Machine Unlearning for Medical Imaging—0
Composable Interventions for Language ModelsCode1
MUSE: Machine Unlearning Six-Way Evaluation for Language ModelsCode4
Releasing Malevolence from Benevolence: The Menace of Benign Data on Machine Unlearning—0
Remembering Everything Makes You Vulnerable: A Limelight on Machine Unlearning for Personalized Healthcare Sector—0
Silver Linings in the Shadows: Harnessing Membership Inference for Machine Unlearning—0
Machine Unlearning Fails to Remove Data Poisoning AttacksCode0
Machine Unlearning with Minimal Gradient Dependence for High Unlearning Ratios—0
AND: Audio Network Dissection for Interpreting Deep Acoustic Models—0
Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning—0
PISTOL: Dataset Compilation Pipeline for Structural Unlearning of LLMs—0
Blind Baselines Beat Membership Inference Attacks for Foundation ModelsCode0
MU-Bench: A Multitask Multimodal Benchmark for Machine UnlearningCode0
Protecting Privacy Through Approximating Optimal Parameters for Sequence Unlearning in Language Models—0
On Newton's Method to Unlearn Neural Networks—0
Textual Unlearning Gives a False Sense of Unlearning—0
GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning—0
Mitigating Social Biases in Language Models through Unlearning—0
Jogging the Memory of Unlearned LLMs Through Targeted Relearning AttacksCode0
Opt-Out: Investigating Entity-Level Unlearning for Large Language Models via Optimal TransportCode0
Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation—0
Cross-Lingual Unlearning of Selective Knowledge in Multilingual Language ModelsCode0
Soft Prompting for Unlearning in Large Language ModelsCode1
Split, Unlearn, Merge: Leveraging Data Attributes for More Effective Unlearning in LLMs—0
Avoiding Copyright Infringement via Large Language Model UnlearningCode0
Towards Efficient Target-Level Machine Unlearning Based on Essential GraphCode0
RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language ModelsCode2
Recent Advances in Federated Learning Driven Large Language Models: A Survey on Architecture, Performance, and Security—0
Potion: Towards Poison UnlearningCode0
A More Practical Approach to Machine Unlearning—0
Unlearning with Control: Assessing Real-world Utility for Large Language Model Unlearning—0
GENIU: A Restricted Data Access Unlearning for Imbalanced Data—0
Decoupling the Class Label and the Target Concept in Machine Unlearning—0
Label Smoothing Improves Machine Unlearning—0
Adversarial Machine Unlearning—0
Machine Unlearning for Uplink Interference Cancellation—0
Alignment Calibration: Machine Unlearning for Contrastive Learning under Auditing—0
RKLD: Reverse KL-Divergence-based Knowledge Distillation for Unlearning Personal Information in Large Language Models—0
What makes unlearning hard and what to do about itCode1
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable—0
Gone but Not Forgotten: Improved Benchmarks for Machine Unlearning—0
An Information Theoretic Evaluation Metric For Strong Unlearning—0
Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach—0
Exploring Fairness in Educational Data Mining in the Context of the Right to be Forgotten—0
Automatic Jailbreaking of the Text-to-Image Generative AI SystemsCode1
Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion ModelsCode2
Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning—0
Machine Unlearning in Large Language ModelsCode1
Unlearning during Learning: An Efficient Federated Machine Unlearning MethodCode1
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