Machine unlearning (MU) is gaining increasing attention due to the need to remove or
modify predictions made by machine learning (ML) models. While training models have …

We explore machine unlearning (MU) in the domain of large language models (LLMs),
referred to as LLM unlearning. This initiative aims to eliminate undesirable data influence …

With evolving data regulations, machine unlearning (MU) has become an important tool for
fostering trust and safety in today's AI models. However, existing MU methods focusing on …

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

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 …

Digital Twins (DTs) can generate digital replicas for mobile networks (MNs) that accurately
reflect the state of MN. Machine learning (ML) models trained in DT for MN (DTMN) virtual …

Abstract Machine unlearning (MU) seeks to remove knowledge of specific data samples
from trained models without the necessity for complete retraining, a task made challenging …

We study the problem of $(\epsilon,\delta) $-certified machine unlearning for minimax
models. Most of the existing works focus on unlearning from standard statistical 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 …

Large Language Models (LLMs) have highlighted the necessity of effective unlearning
mechanisms to comply with data regulations and ethical AI practices. LLM unlearning aims …