As the number of AI systems keeps growing, it is fundamental to implement and develop
efficient control mechanisms to ensure the safe and proper functioning of machine learning …

Abstract Out-of-distribution (OOD) detection is a rapidly growing field due to new robustness
and security requirements driven by an increased number of AI-based systems. Existing …

When working with textual data, a natural application of disentangled representations is fair
classification where the goal is to make predictions without being biased (or influenced) by …

The landscape of available textual adversarial attacks keeps growing, posing severe threats
and raising concerns regarding the deep NLP system's integrity. However, the crucial …

Scientific modeling applications often require estimating a distribution of parameters
consistent with a dataset of observations-an inference task also known as source distribution …

One of the pursued objectives of deep learning is to provide tools that learn abstract
representations of reality from the observation of multiple contextual situations. More …

Quantifying the difference between probability distributions is crucial in machine learning.
However, estimating statistical divergences from empirical samples is challenging due to …

The problem of designing a privacy-preserving camera (PPC) is considered. Previous
designs rely on a static point spread function (PSF), optimized to prevent detection of private …

Distributed representations provide a vector space that captures meaningful relationships
between data instances. The distributed nature of these representations, however …

The proliferation of multimodal data provides a valuable repository of information for
financial distress prediction. However, the use of multimodal data faces critical challenges …