2020

  1. Distributionally Robust Formulation and Model Selection for the Graphical Lasso, Pedro Cisneros-Velarde, Sang-Yun Oh, and Alexander, Petersen, To appear in AISTATS 2020, 2020 [abstract] [url]

2019

  1. A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data, Kshitij Khare, Sang-Yun Oh, Syed Rahman, and Bala, Rajaratnam, Machine Learning, 2019 [abstract] [doi]
  2. Partial Separability and Functional Graphical Models for Multivariate Gaussian Processes, Javier Zapata, Sang-Yun Oh, and Alexander, Petersen, Submitted, [abstract] [url]

2018

  1. Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation, Penporn Koanantakool, Alnur Ali, Ariful Azad, Aydin Buluc, Dmitriy Morozov, Leonid Oliker, Katherine Yelick, and Sang-Yun Oh, In Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, 2018 [abstract] [url] [software]

2017

  1. Generalized Pseudolikelihood Methods for Inverse Covariance Estimation, Alnur Ali, Kshitij Khare, Sang-Yun Oh, and Bala, Rajaratnam, In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017 [abstract] [url]
  2. Exploring Raw HEP Data using Deep Neural Networks at NERSC, Wahid Bhimji, Evan Racah, Seyoon Ko, Peter Sadowski, Craig Tull, Sang-Yun Oh, and , Prabhat, In Proceedings of 38th International Conference on High Energy Physics — PoS(ICHEP2016), 2017 [abstract] [doi]

2016

  1. Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks, Evan Racah, Seyoon Ko, Peter Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, and , Prabhat, In International Conference on Machine Learning and Applications (ICMLA), 2016 [abstract] [doi]
  2. Communication-Avoiding Parallel Sparse-Dense Matrix-Matrix Multiplication, Penporn Koanantakool, Ariful Azad, Aydin Buluc, Dmitriy Morozov, Sang-Yun Oh, Leonid Oliker, and Katherine, Yelick, In 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2016 [abstract] [doi]

2014

  1. Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection, Sang-Yun Oh, Onkar Dalal, Kshitij Khare, and Bala, Rajaratnam, 2014 [abstract] [url] [software]
  2. A convex pseudolikelihood framework for high dimensional partial correlation estimation with convergence guarantees, Kshitij Khare, Sang-Yun Oh, and Bala, Rajaratnam, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2014 [abstract] [doi] [software]