Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection. - Sang-Yun Oh; Onkar Dalal; Kshitij Khare; Bala Rajaratnam (NIPS, 2014)

Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks. - Evan Racah; Seyoon Ko; Peter J. Sadowski; Wahid Bhimji; Craig Tull; Sang-Yun Oh; Pierre Baldi; Prabhat (ICMLA, 2016)

Communication-Avoiding Parallel Sparse-Dense Matrix-Matrix Multiplication. - Penporn Koanantakool; Ariful Azad; Aydin Buluç; Dmitriy Morozov; Sang-Yun Oh; Leonid Oliker; Katherine A. Yelick (IPDPS, 2016)

Generalized Pseudolikelihood Methods for Inverse Covariance Estimation. - Alnur Ali; Kshitij Khare; Sang-Yun Oh; Bala Rajaratnam (AISTATS, 2017)

Communication-Avoiding Optimization Methods for Massive-Scale Graphical Model Structure Learning. - Penporn Koanantakool; Alnur Ali; Ariful Azad; Aydin Buluç; Dmitriy Morozov; Sang-Yun Oh; Leonid Oliker; Katherine A. Yelick (CoRR, 2017)

Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation. - Penporn Koanantakool; Alnur Ali; Ariful Azad; Aydin Buluç; Dmitriy Morozov; Leonid Oliker; Katherine A. Yelick; Sang-Yun Oh (AISTATS, 2018)

A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data. - Kshitij Khare; Sang-Yun Oh; Syed Rahman; Bala Rajaratnam (Mach. Learn., 2019)

Distributionally Robust Formulation and Model Selection for the Graphical Lasso. - Pedro Cisneros-Velarde; Alexander Petersen; Sang-Yun Oh (AISTATS, 2020)

Learning Gaussian graphical models with latent confounders. - Ke Wang 035; Alexander Franks; Sang-Yun Oh (J. Multivar. Anal., 2023)

Learning Massive-scale Partial Correlation Networks in Clinical Multi-omics Studies with HP-ACCORD. - Sungdong Lee; Joshua Bang; Youngrae Kim; Hyungwon Choi; Sang-Yun Oh; Joong-Ho Won (CoRR, 2024)