Gaussian mixture models (GMMs) are fundamental tools in statistical and data sciences. We
study the moments of multivariate Gaussians and GMMs. The $ d $-th moment of an $ n …

We introduce the broad subclass of algebraic compressed sensing problems, where
structured signals are modeled either explicitly or implicitly via polynomials. This includes …

The identifiability problem arises naturally in a number of contexts in mathematics and
computer science. Specific instances include local or global rigidity of graphs and unique …

We introduce the broad subclass of algebraic compressed sensing problems, where
structured signals are modeled either explicitly or implicitly via polynomials. This includes …

Mixtures of ranking models are standard tools for ranking problems. However, even the
fundamental question of parameter identifiability is not fully understood: the identifiability of a …

Motivated by previous work on moment varieties for Gaussian distributions and their
mixtures, we study moment varieties for two other statistically important two-parameter …

We show that the parameters of a $ k $-mixture of inverse Gaussian or gamma distributions
are algebraically identifiable from the first $3 k-1$ moments, and rationally identifiable from …

We resolve most cases of identifiability from sixth-order moments for Gaussian mixtures on
spaces of large dimensions. Our results imply that for a mixture of m Gaussians on an n …