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Learning Gaussian Graphical Models with Latent Confounders

May 14, 2021
Ke Wang, Alexander Franks, Sang-Yun Oh

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Partial Separability and Functional Graphical Models for Multivariate Gaussian Processes

Oct 23, 2019
Javier Zapata, Sang-Yun Oh, Alexander Petersen

* 39 pages, 5 figures 

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Distributionally Robust Formulation and Model Selection for the Graphical Lasso

May 22, 2019
Pedro Cisneros-Velarde, Sang-Yun Oh, Alexander Petersen

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Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation

Apr 08, 2018
Penporn Koanantakool, Alnur Ali, Ariful Azad, Aydin Buluc, Dmitriy Morozov, Leonid Oliker, Katherine Yelick, Sang-Yun Oh

* Main paper: 15 pages, appendix: 24 pages 

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Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks

Dec 06, 2016
Evan Racah, Seyoon Ko, Peter Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat

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Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection

Sep 12, 2014
Sang-Yun Oh, Onkar Dalal, Kshitij Khare, Bala Rajaratnam

* NIPS accepted version 

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A convex pseudo-likelihood framework for high dimensional partial correlation estimation with convergence guarantees

Aug 14, 2014
Kshitij Khare, Sang-Yun Oh, Bala Rajaratnam

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