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 …

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 …

We study how to perform unlearning, ie forgetting undesirable (mis) behaviors, on large
language models (LLMs). We show at least three scenarios of aligning LLMs with human …

The rapid advancement of Large Language Models (LLMs) has demonstrated their vast
potential across various domains, attributed to their extensive pretraining knowledge and …

Machine Unlearning (MU) algorithms have become increasingly critical due to the
imperative adherence to data privacy regulations. The primary objective of MU is to erase …

Abstract 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 …

The aim of Machine Unlearning (MU) is to provide theoretical guarantees on the removal of
the contribution of a given data point from a training procedure. Federated Unlearning (FU) …

Traffic sign recognition (TSR) is vital for vehicle safety and navigation, especially in the era
of autonomous cars. Internet of Vehicles (IoV) provide a promising infrastructure for …

Machine unlearning is the process of efficiently removing the influence of a training data
instance from a trained machine learning model without retraining it from scratch. A popular …

Machine\emph {unlearning}, which involves erasing knowledge about a\emph {forget set}
from a trained model, can prove to be costly and infeasible by existing techniques. We …