With the rapid development of low-cost consumer electronics and pervasive adoption of next
generation wireless communication technologies, a tremendous amount of data has been …

Graph neural networks (GNNs) are a type of deep learning models that are trained on
graphs and have been successfully applied in various domains. Despite the effectiveness of …

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 …

In response to recent data regulation requirements, machine unlearning (MU) has emerged
as a critical process to remove the influence of specific examples from a given model …

This paper explores the problem of Generalist Anomaly Detection (GAD) aiming to train one
single detection model that can generalize to detect anomalies in diverse datasets from …

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

In recent years, large language models (LLMs) have spurred a new research paradigm in
natural language processing. Despite their excellent capability in knowledge-based …

Large Language Models (LLMs) often memorize sensitive, private, or copyrighted data
during pre-training. LLM unlearning aims to eliminate the influence of undesirable data from …

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

In recent years, the notion of``the right to be forgotten"(RTBF) has evolved into a
fundamental element of data privacy regulations, affording individuals the ability to request …