118 citations · 689 across the 53 of their papers we have counts for
4 papers · 1 filter
Scalable Bayesian shrinkage and uncertainty quantification in 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…
Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection
Sang-Yun Oh, Onkar Dalal, Kshitij Khare +1
Sparse high dimensional graphical model selection is a popular topic in contemporary machine learning. To this end, various useful approaches have been proposed in the context of $…
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…
Iterative Thresholding Algorithm for Sparse Inverse Covariance Estimation
Dominique Guillot, Bala Rajaratnam, Benjamin T. Rolfs +2
The L1-regularized maximum likelihood estimation problem has recently become a topic of great interest within the machine learning, statistics, and optimization communities as a me…