1.3k citations · 2.1k across the 14 of their papers we have counts for
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Family-wise error rate control in Gaussian graphical model selection via Distributionally Robust Optimization
Chau Tran, Pedro Cisneros-Velarde, Sang-Yun Oh +1
Recently, a special case of precision matrix estimation based on a distributionally robust optimization (DRO) framework has been shown to be equivalent to the graphical lasso. From…
Learning Gaussian Graphical Models with Latent Confounders
Ke Wang, Alexander Franks, Sang-Yun Oh
Gaussian Graphical models (GGM) are widely used to estimate the network structures in many applications ranging from biology to finance. In practice, data is often corrupted by lat…
Partial Separability and Functional Graphical Models for Multivariate Gaussian Processes
Javier Zapata, Sang-Yun Oh, Alexander Petersen
The covariance structure of multivariate functional data can be highly complex, especially if the multivariate dimension is large, making extensions of statistical methods for stan…
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…
Towards a sparse, scalable, and stably positive definite (inverse) covariance estimator
Sang-Yun Oh, Bala Rajaratnam, Joong-Ho Won
High dimensional covariance estimation and graphical models is a contemporary topic in statistics and machine learning having widespread applications. An important line of research…
A convex pseudo-likelihood framework for high dimensional partial correlation estimation with convergence guarantees
Kshitij Khare, Sang-Yun Oh, Bala Rajaratnam
Sparse high dimensional graphical model selection is a topic of much interest in modern day statistics. A popular approach is to apply l1-penalties to either (1) parametric likelih…