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 …

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 …

Studying adversarial attacks on Reinforcement Learning (RL) agents has become a key
aspect of developing robust, RL-based solutions. Test-time attacks, which target the post …

Today, computer systems hold large amounts of personal data. Yet while such an
abundance of data allows breakthroughs in artificial intelligence, and especially machine …

As the right to be forgotten has been legislated worldwide, many studies attempt to design
unlearning mechanisms to protect users' privacy when they want to leave machine learning …

Personal digital data is a critical asset, and governments worldwide have enforced laws and
regulations to protect data privacy. Data users have been endowed with the right to be …

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

There has been a growing interest in Machine Unlearning recently, primarily due to legal
requirements such as the General Data Protection Regulation (GDPR) and the California …

As a new paradigm to erase data from a model and protect user privacy, machine
unlearning has drawn significant attention. However, existing studies on machine …