In this paper, we present an overview of constrained parallel factor (PARAFAC) models
where the constraints model linear dependencies among columns of the factor matrices of …

Tensor decompositions are at the core of many blind source separation (BSS) algorithms,
either explicitly or implicitly. In particular, the canonical polyadic (CP) tensor decomposition …

Large-scale problems have always been a challenge for numerical computations. An
example is the treatment of fully populated n× n matrices when n2 is close to or beyond the …

Abstract Generalized Additive Models (GAMs) have quickly become the leading choice for
interpretable machine learning. However, unlike uninterpretable methods such as DNNs …

The notion of a tensor captures three great ideas: equivariance, multilinearity, separability.
But trying to be three things at once makes the notion difficult to understand. We will explain …

In this paper we offer a review and bibliography of work on Hankel low-rank approximation
and completion, with particular emphasis on how this methodology can be used for time …

We consider the problem of determining the symmetric tensor rank for symmetric tensors
with an algebraic geometry approach. We give algorithms for computing the symmetric rank …

We investigate properties of the determinants of tensors, and their applications in the
eigenvalue theory of tensors. We show that the determinant inherits many properties of the …

We propose a novel approach for hyperspectral super-resolution, that is based on low-rank
tensor approximation for a coupled low-rank multilinear (Tucker) model. We show that the …

We consider here the problem, which is quite classical in Algebraic geometry, of studying
the secant varieties of a projective variety X. The case we concentrate on is when X is a …