Understanding multivariate dependencies in both the bulk and the tails of a distribution is an
important problem for many applications, such as ensuring algorithms are robust to …

In this article we propose a conceptual framework to study ensembles of conformal
predictors (CP), that we call Ensemble Predictors (EP). Our approach is inspired by the …

Change detection (CD) in heterogeneous remote sensing images has been widely used for
disaster monitoring and land-use management. In the past decade, the heterogeneous CD …

Copulas are a powerful statistical tool that captures dependencies across data dimensions.
When applying Copulas, we can estimate multivariate distribution functions by initially …

In survival analysis, subjects often face competing risks; for example, individuals with cancer
may also suffer from heart disease or other illnesses, which can jointly influence the …

Spectral Kernel Networks (SKNs) emerge as a promising approach in machine learning,
melding solid theoretical foundations of spectral kernels with the representation power of …

This thesis discusses applications of probabilistic and connectionist models for constructing
and training cumulative distribution functions (CDFs). First, it is shown how existing tools …

Copulas are popular in high-dimensional statistical applications as they allow for
dependence modeling with arbitrary margins. They are also used in rare event analysis …