3 citations · 11 across the 7 of their papers we have counts for
7 papers
A computational approach to maximum likelihood thresholds for colored Gaussian graphical models
Roser Homs, Olga Kuznetsova, Bernadette J. Stolz
Gaussian graphical models (GGMs) are essential tools for interpretable structure learning. However, in high-dimensional, small-sample regimes, the available data is often insuffici…
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…
Combinatorial Derived Matroids
Olga Kuznetsova, Ragnar Freij-Hollanti, Relinde Jurrius
Let be an arbitrary matroid with circuits . We propose a definition of a derived matroid that has as its ground set . Unlike previous attem…
Algebraic degree of optimization over a variety with an application to -norm distance degree
Kaie Kubjas, Olga Kuznetsova, Luca Sodomaco
We study an optimization problem with the feasible set being a real algebraic variety and whose parametric objective function is gradient-solvable with respect to the par…
Computing Maximum Likelihood Estimates for Gaussian Graphical Models with Macaulay2
Carlos Améndola, Luis David García Puente, Roser Homs +2
We introduce the package "GraphicalModelsMLE" for computing the maximum likelihood estimates (MLEs) of a Gaussian graphical model in the computer algebra system Macaulay2. This pac…
Information Hiding Using Matroid Theory
Ragnar Freij-Hollanti, Olga Kuznetsova
Inspired by problems in Private Information Retrieval, we consider the setting where two users need to establish a communication protocol to transmit a secret without revealing it…
Exact Solutions in Log-Concave Maximum Likelihood Estimation
Alexandros Grosdos, Alexander Heaton, Kaie Kubjas +3
We study probability density functions that are log-concave. Despite the space of all such densities being infinite-dimensional, the maximum likelihood estimate is the exponential…