Machine unlearning has become a pivotal task to erase the influence of data from a trained
model. It adheres to recent data regulation standards and enhances the privacy and security …

Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious
data, posing risks of privacy breaches, security vulnerabilities, and performance …

Removing information from a machine learning model is a non-trivial task that requires to
partially revert the training process. This task is unavoidable when sensitive data, such as …

Machine Unlearning, the process of selectively eliminating the influence of certain data
examples used during a model's training, has gained significant attention as a means for …

The rise of the phenomenon of the “right to be forgotten” has prompted research on machine
unlearning, which grants data owners the right to actively withdraw data that has been used …

Removing the influence of a specified subset of training data from a machine learning model
may be required to address issues such as privacy, fairness, and data quality. Retraining the …

Machine unlearning, an emerging research topic focusing on compliance with data privacy
regulations, enables trained models to remove the information learned from specific data …

The trustworthy machine learning (ML) community is increasingly recognizing the crucial
need for models capable of selectively'unlearning'data points after training. This leads to the …

Machine unlearning, the ability for a machine learning model to forget, is becoming
increasingly important to comply with data privacy regulations, as well as to remove harmful …

Machine unlearning refers to the process of mitigating the influence of specific training data
on machine learning models based on removal requests from data owners. However, one …