Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Forgetting refers to the loss or deterioration of previously acquired knowledge. While
existing surveys on forgetting have primarily focused on continual learning, forgetting is a …

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

Despite the ability to train capable LLMs, the methodology for maintaining their relevancy
and rectifying errors remains elusive. To this end, the past few years have witnessed a surge …

This paper delves into the realm of ChatGPT, an AI-powered chatbot that utilizes topic
modeling and reinforcement learning to generate natural responses. Although ChatGPT …

The increasing data privacy concerns in recommendation systems have made federated
recommendations attract more and more attention. Existing federated recommendation …

Federated machine unlearning (FMU) aims to remove the influence of a specified subset of
training data upon request from a trained federated learning model. Despite achieving …

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

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 …

The right to be forgotten calls for efficient machine unlearning techniques that make trained
machine learning models forget a cohort of data. The combination of training and unlearning …