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

Language models (LMs) are trained on vast amounts of text data, which may include private
and copyrighted content. Data owners may request the removal of their data from a trained …

The recent advances in diffusion models (DMs) have revolutionized the generation of
realistic and complex images. However, these models also introduce potential safety …

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 …

Generative AI technologies have been deployed in many places, such as (multimodal) large
language models and vision generative models. Their remarkable performance should be …

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 …

With increasing privacy concerns in artificial intelligence, regulations have mandated the
right to be forgotten, granting individuals the right to withdraw their data from models …

Visual diffusion models have revolutionized the field of creative AI, producing high-quality
and diverse content. However, they inevitably memorize training images or videos …

The need for effective unlearning mechanisms in large language models (LLMs) is
increasingly urgent, driven by the necessity to adhere to data regulations and foster ethical …

Multimodal contrastive learning uses various data modalities to create high-quality features,
but its reliance on extensive data sources on the Internet makes it vulnerable to backdoor …