118 citations · 689 across the 53 of their papers we have counts for
57 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…
Pseudo-likelihood Estimators for Graphical Models: Existence and Uniqueness
Benjamin Roycraft, Bala Rajaratnam
Graphical and sparse (inverse) covariance models have found widespread use in modern sample-starved high dimensional applications. A part of their wide appeal stems from the signif…
Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion
Han Wu, Jie Yin, Bala Rajaratnam +1
Knowledge graphs (KGs) are powerful in terms of their inference abilities, but are also notorious for their incompleteness and long-tail distribution of relations. To address these…
A unified framework for correlation mining in ultra-high dimension
Yun Wei, Bala Rajaratnam, Alfred O. Hero
Many applications benefit from theory relevant to the identification of variables having large correlations or partial correlations in high dimension. Recently there has been progr…
The critical exponent: a novel graph invariant
Dominique Guillot, Apoorva Khare, Bala Rajaratnam
A surprising result of FitzGerald and Horn (1977) shows that is positive semidefinite (p.s.d.) for every entrywise nonnegative p.s.d. matri…