16 citations · 42 across the 6 of their papers we have counts for
8 papers
Large Scale Partial Correlation Screening with Uncertainty Quantification
Emily Neo, Peter Radchenko, Bala Rajaratnam
Identifying multivariate dependencies in high-dimensional data is an important problem in large-scale inference. This problem has motivated recent advances in mining (partial) corr…
Scalable and non-iterative graphical model estimation
Kshitij Khare, Syed Rahman, Bala Rajaratnam +1
Graphical models have found widespread applications in many areas of modern statistics and machine learning. Iterative Proportional Fitting (IPF) and its variants have become the d…
A convex framework for high-dimensional sparse Cholesky based covariance estimation
Kshitij Khare, Sang Oh, Syed Rahman +1
Covariance estimation for high-dimensional datasets is a fundamental problem in modern day statistics with numerous applications. In these high dimensional datasets, the number of…
Model-free consistency of graph partitioning
Peter Diao, Dominique Guillot, Apoorva Khare +1
In this paper, we exploit the theory of dense graph limits to provide a new framework to study the stability of graph partitioning methods, which we call structural consistency. Bo…
The Letac-Massam conjecture and existence of high dimensional Bayes estimators for Graphical Models
Emanuel Ben-David, Bala Rajaratnam
In recent years, a variety of useful extensions of the Wishart have been proposed in the literature for the purposes of studying Markov random fields/graphical models. In particula…
G-AMA: Sparse Gaussian graphical model estimation via alternating minimization
Onkar Dalal, Bala Rajaratnam
Several methods have been recently proposed for estimating sparse Gaussian graphical models using regularization on the inverse covariance matrix. Despite recent advance…