Sparse polynomial chaos expansions (PCE) are a popular surrogate modelling method that
takes advantage of the properties of PCE, the sparsity-of-effects principle, and powerful …

Data valuation arises as a non-trivial challenge in real-world use cases such as
collaborative machine learning, federated learning, trusted data sharing, data marketplaces …

We present a novel procedure for optimization based on the combination of efficient
quantized tensor train representation and a generalized maximum matrix volume principle …

This paper considers the use of robust principal component analysis (RPCA) in a CUR
decomposition framework and applications thereof. Our main algorithms produce a robust …

Sobol indices are a widespread quantitative measure for variance-based global sensitivity
analysis, but computing and utilizing them remains challenging for high-dimensional …

We propose a parallel version of the cross interpolation algorithm and apply it to calculate
high-dimensional integrals motivated by Ising model in quantum physics. In contrast to …

Characterising intractable high-dimensional random variables is one of the fundamental
challenges in stochastic computation. The recent surge of transport maps offers a …

Low rank tensor approximation is a fundamental tool in modern machine learning and data
science. In this paper, we study the characterization, perturbation analysis, and an efficient …

Emerging tensor network techniques for solutions of partial differential equations (PDEs),
known for their ability to break the curse of dimensionality, deliver new mathematical …

Nonnegative matrix factorization (NMF) is a linear dimensionality technique for nonnegative
data with applications such as image analysis, text mining, audio source separation, and …