The unknown parameters (variance, smoothness, and covariance length) of a spatial
covariance function can be estimated by maximizing the joint Gaussian log-likelihood …

Efficient and interpretable spatial analysis is crucial in many fields such as geology, sports,
and climate science. Tensor latent factor models can describe higher-order correlations for …

We solve a weakly supervised regression problem. Under “weakly” we understand that for
some training points the labels are known, for some unknown, and for others uncertain due …

In this paper, we introduce the operator dependent range-separated (RS) tensor
approximation of the discretized Dirac delta function (distribution) in R d. It is constructed by …

Very often, in the course of uncertainty quantification tasks or data analysis, one has to deal
with high-dimensional random variables (RVs). A high-dimensional RV can be described by …

In this paper, we introduce a method for multivariate function approximation using function
evaluations, Chebyshev polynomials, and tensor-based compression techniques via the …

We consider a weakly supervised classification problem. It is a classification problem where
the target variable can be unknown or uncertain for some subset of samples. This problem …

Very often, in the course of uncertainty quantification tasks or data analysis, one has to deal
with high‐dimensional random variables. Here the interest is mainly to compute …

We present a novel framework for the probabilistic modelling of random fourth order material
tensor fields, with a focus on tensors that are physically symmetric and positive definite …

Given a sample of iid high-dimensional centered random vectors, we consider a problem of
estimation of their covariance matrix $\Sigma $ with an additional assumption that $\Sigma …