2 citations · 3 across the 9 of their papers we have counts for
9 papers
Decomposing Determinantal Varieties from Statistics via Matroid Theory
Per Alexandersson, Yulia Alexandr, Emiliano Liwski +2
We study determinantal varieties from conditional independence models with hidden variables, focusing on their irreducible decompositions, dimensions, degrees, and Gröbner bases. E…
Characteristic Imsets for Cyclic Linear Causal Models and the Chickering Ideal
Joseph Johnson, Pardis Semnani
Two directed graphs are called covariance equivalent if they induce the same set of covariance matrices, up to a Lebesgue measure zero set, on the random variables of their associa…
Path-Dependent SDEs: Solutions and Parameter Estimation
Pardis Semnani, Vincent Guan, Elina Robeva +1
We develop a consistent method for estimating the parameters of a rich class of path-dependent SDEs, called signature SDEs, which can model general path-dependent phenomena. Path s…
Decomposing conditional independence ideals with hidden variables
Yulia Alexandr, Kristen Dawson, Hannah Friedman +3
We study a family of determinantal ideals whose decompositions encode the structural zeros in conditional independence models with hidden variables. We provide explicit decompositi…
Homaloidal Polynomials and Gaussian Models of Maximum Likelihood Degree One
Shelby Cox, Pratik Misra, Pardis Semnani
We study the Gaussian statistical models whose log-likelihood function has a unique complex critical point, i.e., has maximum likelihood degree one. We exploit the connection devel…
An exponential bound for simultaneous embeddings of planar graphs
Ritesh Goenka, Pardis Semnani, Chi Hoi Yip
We show that there are planar graphs on vertices which do not admit a simultaneous straight-line embedding on any -point set in the plane. In particula…
Causal Structure Learning in Directed, Possibly Cyclic, Graphical Models
Pardis Semnani, Elina Robeva
We consider the problem of learning a directed graph from observational data. We assume that the distribution which gives rise to the samples is Markov and faithful to th…
Log-concave density estimation in undirected graphical models
Kaie Kubjas, Olga Kuznetsova, Elina Robeva +2
We study the problem of maximum likelihood estimation of densities that are log-concave and lie in the graphical model corresponding to a given undirected graph . We show that t…
The Set of Orthogonal Tensor Trains
Pardis Semnani, Elina Robeva
In this paper we study the set of tensors that admit a special type of decomposition called an orthogonal tensor train decomposition. Finding equations defining varieties of low-ra…