Recently, an increasing number of laws have governed the useability of users' privacy. For
example, Article 17 of the General Data Protection Regulation (GDPR), the right to be …

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

A fundamental question in theoretical machine learning is generalization. Over the past
decades, the PAC-Bayesian approach has been established as a flexible framework to …

Personal digital data is a critical asset, and governments worldwide have enforced laws and
regulations to protect data privacy. Data users have been endowed with the right to be …

Variational particle-based Bayesian learning methods have the advantage of not being
limited by the bias affecting more conventional parametric techniques. This paper proposes …

The rapid progress of AI, combined with its unprecedented public adoption and the
propensity of large neural networks to memorize training data, has given rise to significant …

In the rapidly evolving landscape of digital assets, the imperative for robust data privacy and
compliance with regulatory frameworks has intensified. This paper investigates the critical …

In recent years, Federated Learning (FL) has garnered significant attention as a distributed
machine learning paradigm. To facilitate the implementation of the right to be forgotten, the …

Machine unlearning, the process of selectively forgetting or removing the influence of
specific data points from a machine learning model, is increasingly important for privacy and …

Recently, an increasing number of laws have governed the useability of users' privacy. For
example, Article 17 of the General Data Protection Regulation (GDPR), the right to be …