Clustering refers to the task of identifying groups or clusters in a data set. In density‐based
clustering, a cluster is a set of data objects spread in the data space over a contiguous …

Rapidly increasing quality of AI-generated content makes it difficult to distinguish between
human and AI-generated texts, which may lead to undesirable consequences for society …

Dealing with uncertainty in applications of machine learning to real-life data critically
depends on the knowledge of intrinsic dimensionality (ID). A number of methods have been …

With the continued digitization of societal processes, we are seeing an explosion in
available data. This is referred to as big data. In a research setting, three aspects of the data …

Recent developments in big data technologies are revolutionizing the field of healthcare
predictive analytics (HPA), enabling researchers to explore challenging problems using …

Most of the existing methods for estimating the local intrinsic dimension of a data distribution
do not scale well to high dimensional data. Many of them rely on a non-parametric nearest …

Classifier explanations have been identified as a crucial component of knowledge
discovery. Local explanations evaluate the behavior of a classifier in the vicinity of a given …

Machine learning systems are vulnerable to adversarial attack. By applying to the input
object a small, carefully-designed perturbation, a classifier can be tricked into making an …

Building fair deep neural networks (DNNs) is a crucial step towards achieving trustworthy
artificial intelligence. Delving into deeper factors that affect the fairness of DNNs is …

Learning the underlying slow and fast dynamics of a system is instrumental for many
practical applications related to the system. However, existing approaches are limited in …