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 …

In recent years, the notion of “the right to be forgotten”(RTBF) has become a crucial aspect of
data privacy for digital trust and AI safety, requiring the provision of mechanisms that support …

Large-scale text-to-image diffusion models can generate high-fidelity images with powerful
compositional ability. However, these models are typically trained on an enormous amount …

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

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

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 …

Deep machine unlearning is the problem of'removing'from a trained neural network a subset
of its training set. This problem is very timely and has many applications, including the key …

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 …

Although large language models (LLMs) are widely deployed, the data used to train them is
rarely disclosed. Given the incredible scale of this data, up to trillions of tokens, it is all but …

Once users have shared their data online, it is generally difficult for them to revoke access
and ask for the data to be deleted. Machine learning (ML) exacerbates this problem because …