Motivation Low-dimensional representations of high-dimensional data are routinely
employed in biomedical research to visualize, interpret and communicate results from …

Machine learning models have shown exceptional prowess in solving complex issues
across various domains. However, these models can sometimes exhibit biased decision …

Flexible Bayesian models are typically constructed using limits of large parametric models
with a multitude of parameters that are often uninterpretable. In this article, we offer a novel …

Neuroimaging data are an increasingly important part of etiological studies of neurological
and psychiatric disorders. However, mitigating the influence of nuisance variables, including …

We commonly encounter the problem of identifying an optimally weight-adjusted version of
the empirical distribution of observed data, adhering to predefined constraints on the …

In recent years, there has been a revolution in data-driven policing. With that has come
scrutiny on how bias in historical data affects algorithmic decision making. In this exploratory …

In many fields, including medicine and biology, there has been in the last years an
increasing diffusion and availability of complex data from different sources. Examples …