Revisiting Machine Unlearning with Dimensional Alignment
Machine unlearning, an emerging research topic focusing on compliance with data privacy
regulations, enables trained models to remove the information learned from specific data …
regulations, enables trained models to remove the information learned from specific data …
Label-agnostic forgetting: A supervision-free unlearning in deep models
S Shen, C Zhang, Y Zhao, A Bialkowski… - arXiv preprint arXiv …, 2024 - arxiv.org
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 …
preserving that of the remaining dataset in a well-trained model. With the increasing …
Silver Linings in the Shadows: Harnessing Membership Inference for Machine Unlearning
With the continued advancement and widespread adoption of machine learning (ML)
models across various domains, ensuring user privacy and data security has become a …
models across various domains, ensuring user privacy and data security has become a …
Towards Understanding the Feasibility of Machine Unlearning
In light of recent privacy regulations, machine unlearning has attracted significant attention
in the research community. However, current studies predominantly assess the overall …
in the research community. However, current studies predominantly assess the overall …
Duck: Distance-based unlearning via centroid kinematics
Machine Unlearning is rising as a new field, driven by the pressing necessity of ensuring
privacy in modern artificial intelligence models. This technique primarily aims to eradicate …
privacy in modern artificial intelligence models. This technique primarily aims to eradicate …
Deep unlearning: Fast and efficient gradient-free class forgetting
Machine unlearning is a prominent and challenging field, driven by regulatory demands for
user data deletion and heightened privacy awareness. Existing approaches involve …
user data deletion and heightened privacy awareness. Existing approaches involve …
Partially Blinded Unlearning: Class Unlearning for Deep Networks a Bayesian Perspective
In order to adhere to regulatory standards governing individual data privacy and safety,
machine learning models must systematically eliminate information derived from specific …
machine learning models must systematically eliminate information derived from specific …
Efficient attribute unlearning: Towards selective removal of input attributes from feature representations
Recently, the enactment of privacy regulations has promoted the rise of the machine
unlearning paradigm. Existing studies of machine unlearning mainly focus on sample-wise …
unlearning paradigm. Existing studies of machine unlearning mainly focus on sample-wise …
Model sparsity can simplify machine unlearning
In response to recent data regulation requirements, machine unlearning (MU) has emerged
as a critical process to remove the influence of specific examples from a given model …
as a critical process to remove the influence of specific examples from a given model …