118 citations · 689 across the 53 of their papers we have counts for
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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…
Generalized Pseudolikelihood Methods for Inverse Covariance Estimation
Alnur Ali, Kshitij Khare, Sang-Yun Oh +1
We introduce PseudoNet, a new pseudolikelihood-based estimator of the inverse covariance matrix, that has a number of useful statistical and computational properties. We show, thro…
Scalable Bayesian shrinkage and uncertainty quantification for high-dimensional regression
Bala Rajaratnam, Doug Sparks, Kshitij Khare +1
Bayesian shrinkage methods have generated a lot of recent interest as tools for high-dimensional regression and model selection. These methods naturally facilitate tractable uncert…
Bayesian inference for Gaussian graphical models beyond decomposable graphs
Kshitij Khare, Bala Rajaratnam, Abhishek Saha
Bayesian inference for graphical models has received much attention in the literature in recent years. It is well known that when the graph G is decomposable, Bayesian inference is…