The tensor train (TT) format enjoys appealing advantages in handling structural high-order
tensors. The recent decade has witnessed the wide applications of TT-format tensors from …

This paper introduces a general framework of Semiparametric TEnsor FActor analysis
(STEFA) that focuses on the methodology and theory of lowrank tensor decomposition with …

The density matrices are positively semi-definite Hermitian matrices of unit trace that
describe the state of a quantum system. The goal of the paper is to develop minimax lower …

High-dimensional linear regression under heavy-tailed noise or outlier corruption is
challenging, both computationally and statistically. Convex approaches have been proven …

Low-rank matrix regression refers to the instances of recovering a low-rank matrix based on
specially designed measurements and the corresponding noisy outcomes. Numerous …

Matrix factorization is a powerful data analysis tool. It has been used in multivariate time
series analysis, leading to the decomposition of the series in a small set of latent factors …

We consider the problem of estimating the mean of a distribution supported by the k-
dimensional probability simplex in the setting where an ε fraction of observations are subject …

Wearable electronics capable of recording and transmitting biosignals can provide
convenient and pervasive health monitoring. A typical EEG recording produces large …

Low-rank matrix estimation under heavy-tailed noise is challenging, both computationally
and statistically. Convex approaches have been proven statistically optimal but suffer from …

We study the maximum likelihood estimator of density of n independent observations, under
the assumption that it is well approximated by a mixture with a large number of components …