Feature selection is essential in the analysis of molecular systems and many other fields, but
several uncertainties remain: What is the optimal number of features for a simplified …

Modern datasets are characterized by numerous features related by complex dependency
structures. To deal with these data, dimensionality reduction techniques are essential. Many …

We propose a sampling algorithm relying on a collective variable (CV) of midsize dimension
modeled by a normalizing flow and using nonequilibrium dynamics to propose full …

Complex networks are powerful mathematical tools for modelling and understanding the
behaviour of highly interconnected systems. However, existing methods for analyzing these …

Real-world datasets characterized by discrete features are ubiquitous: from categorical
surveys to clinical questionnaires, from unweighted networks to DNA sequences …

Understanding the intricacies of factors influencing European Union Emission Trading
System (EU ETS) market prices is paramount for effective policy making and strategy …

Atomic simulations of materials require significant resources to generate, store, and analyze.
Here, descriptor functions are proposed as a general, metric latent space for atomic …

Clinical databases typically include, for each patient, many heterogeneous features, for
example blood exams, the clinical history before the onset of the disease, the evolution of …

Galaxies are complex objects, yet the number of independent parameters to describe them
remains unknown. We present here a non-parametric method to estimate the intrinsic …

We introduce an approach which allows detecting causal relationships between variables
for which the time evolution is available. Causality is assessed by a variational scheme …