1 citations · 1 across the 5 of their papers we have counts for
5 papers
Robust Continual Unlearning against Knowledge Erosion and Forgetting Reversal
Eun-Ju Park, Youjin Shin, Simon S. Woo
As a means to balance the growth of the AI industry with the need for privacy protection, machine unlearning plays a crucial role in realizing the ``right to be forgotten'' in arti…
Efficient Unlearning through Maximizing Relearning Convergence Delay
Khoa Tran, Simon S. Woo
Machine unlearning poses challenges in removing mislabeled, contaminated, or problematic data from a pretrained model. Current unlearning approaches and evaluation metrics are sole…
Suppression or Deletion: A Restoration-Based Representation-Level Analysis of Machine Unlearning
Yurim Jang, Jaeung Lee, Dohyun Kim +2
As pretrained models are increasingly shared on the web, ensuring that models can forget or delete sensitive, copyrighted, or private information upon request has become crucial. M…
Fairness and Robustness in Machine Unlearning
Khoa Tran, Simon S. Woo
Machine unlearning poses the challenge of ``how to eliminate the influence of specific data from a pretrained model'' in regard to privacy concerns. While prior research on approxi…
Layer Attack Unlearning: Fast and Accurate Machine Unlearning via Layer Level Attack and Knowledge Distillation
Hyunjune Kim, Sangyong Lee, Simon S. Woo
Recently, serious concerns have been raised about the privacy issues related to training datasets in machine learning algorithms when including personal data. Various regulations i…