24 citations · 73 across the 28 of their papers we have counts for
8 papers · 1 filter
Beyond independent component analysis: identifiability and algorithms
Alvaro Ribot, Anna Seigal, Piotr Zwiernik
Independent Component Analysis (ICA) is a classical method for recovering latent variables with useful identifiability properties. For independent variables, cumulant tensors are d…
Contrastive independent component analysis
Kexin Wang, Aida Maraj, Anna Seigal
In recent years, there has been growing interest in jointly analyzing a foreground dataset, representing an experimental group, and a background dataset, representing a control gro…
Identifiability of overcomplete independent component analysis
Kexin Wang, Anna Seigal
Independent component analysis (ICA) studies mixtures of independent latent sources. An ICA model is identifiable if the mixing can be recovered uniquely. It is well-known that ICA…
Complete collineations for maximum likelihood estimation
Gergely Bérczi, Eloise Hamilton, Philipp Reichenbach +1
We import the algebro-geometric notion of a complete collineation into the study of maximum likelihood estimation in directed Gaussian graphical models. A complete collineation pro…
Symmetries in Directed Gaussian Graphical Models
Visu Makam, Philipp Reichenbach, Anna Seigal
We define Gaussian graphical models on directed acyclic graphs with coloured vertices and edges, calling them RDAG (restricted directed acyclic graph) models. If two vertices or ed…
Toric invariant theory for maximum likelihood estimation in log-linear models
Carlos Améndola, Kathlén Kohn, Philipp Reichenbach +1
We establish connections between invariant theory and maximum likelihood estimation for discrete statistical models. We show that norm minimization over a torus orbit is equivalent…
Invariant theory and scaling algorithms for maximum likelihood estimation
Carlos Améndola, Kathlén Kohn, Philipp Reichenbach +1
We uncover connections between maximum likelihood estimation in statistics and norm minimization over a group orbit in invariant theory. We focus on Gaussian transformation familie…
Duality of Graphical Models and Tensor Networks
Elina Robeva, Anna Seigal
In this article we show the duality between tensor networks and undirected graphical models with discrete variables. We study tensor networks on hypergraphs, which we call tensor h…