Copulas are a powerful tool for modeling multivariate distributions as they allow to
separately estimate the univariate marginal distributions and the joint dependency structure …

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 …

Normalizing flows, a popular class of deep generative models, often fail to represent
extreme phenomena observed in real-world processes. In particular, existing normalizing …

Censoring is the central problem in survival analysis where either the time-to-event (for
instance, death), or the time-to censoring (such as loss of follow-up) is observed for each …

Weather derivatives are used to protect farmers in sub-Saharan Africa (SSA) from yield loss
caused by climate change. However, mispricing these contracts poses a significant risk due …

The Copula is widely used to describe the relationship between the marginal distribution
and joint distribution of random variables. The estimation of high-dimensional Copula is …

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 …

We propose a new generative modeling technique for learning multidimensional cumulative
distribution functions (CDFs) in the form of copulas. Specifically, we consider certain classes …

We propose a new sensitivity analysis model that combines copulas and normalizing flows
for causal inference under unobserved confounding. We refer to the new model as $\rho …

Generative moment matching networks (GMMNs) are introduced as dependence models for
the joint innovation distribution of multivariate time series (MTS). Following the popular …