A large portion of data mining and analytic services use modern machine learning techniques,
such as deep learning. The state-of-the-art results by deep learning come at the price of …

Existing approaches to distribute Generative Adversarial Networks (GANs) either (i) fail to
scale for they typically put the two components of a GAN (the generator and the discriminator) …

It is known that LLMs do hallucinate, that is, they return incorrect information as facts. In this
paper, we introduce the possibility to study these hallucinations under a structured form: …

The concept of explainability is envisioned to satisfy society's demands for transparency on
machine learning decisions. The concept is simple: like humans, algorithms should explain …

Among the many technical challenges to enforcing AI regulations, one crucial yet underexplored
problem is the risk of audit manipulation. This manipulation occurs when a platform …

Remote change detection in LLMs is a difficult problem. Existing methods are either too
expensive for deployment at scale, or require initial white-box access to model weights or grey-…

Large Language Models (LLMs) are nowadays prompted for a wide variety of tasks. In this
article, we investigate their ability in reciting and generating graphs. We first study the ability …

Energy consumption is the most important factor that determines sensor node lifetime. The
optimization of wireless sensor network lifetime targets not only the reduction of energy …

We address the problem of watermarking graph objects, which consists in hiding information
within them, to prove their origin. The two existing methods to watermark graphs use …

Recent advances in the fingerprinting of deep neural networks detect instances of models,
placed in a black-box interaction scheme. Inputs used by the fingerprinting protocols are …