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Machine Unlearning for Streaming Forgetting
Shaofei Shen, Chenhao Zhang, Yawen Zhao +3
Machine unlearning aims to remove knowledge of the specific training data in a well-trained model. Currently, machine unlearning methods typically handle all forgetting data in a s…
Toward Efficient Data-Free Unlearning
Chenhao Zhang, Shaofei Shen, Weitong Chen +1
Machine unlearning without access to real data distribution is challenging. The existing method based on data-free distillation achieved unlearning by filtering out synthetic sampl…
GENIU: A Restricted Data Access Unlearning for Imbalanced Data
Chenhao Zhang, Shaofei Shen, Yawen Zhao +2
With the increasing emphasis on data privacy, the significance of machine unlearning has grown substantially. Class unlearning, which involves enabling a trained model to forget da…
Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Models
Shaofei Shen, Chenhao Zhang, Yawen Zhao +3
Machine unlearning aims to remove information derived from forgotten data while preserving that of the remaining dataset in a well-trained model. With the increasing emphasis on da…
CaMU: Disentangling Causal Effects in Deep Model Unlearning
Shaofei Shen, Chenhao Zhang, Alina Bialkowski +2
Machine unlearning requires removing the information of forgetting data while keeping the necessary information of remaining data. Despite recent advancements in this area, existin…