Publications

2020

  • Cisneros-Velarde, P., Petersen, A. & Oh, S.-Y. Distributionally Robust Formulation and Model Selection for the Graphical Lasso. in Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (eds. Chiappa, S. & Calandra, R.) vol. 108 756–765 (PMLR, 2020).

2019

  • Khare, K., Oh, S.-Y., Rahman, S. & Rajaratnam, B. A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data. Machine Learning 108, 2061–2086 (2019).
    doi
  • Zapata, J., Oh, S.-Y. & Petersen, A. Partial Separability and Functional Graphical Models for Multivariate Gaussian Processes. Submitted (2019).

2018

  • Koanantakool, P., Ali, A., Azad, A., Buluc, A., Morozov, D., Oliker, L., Yelick, K. & Oh, S.-Y. Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation. in Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (eds. Storkey, A. & Perez-Cruz, F.) vol. 84 1376–1386 (PMLR, 2018).

2017

  • Bhimji, W., Racah, E., Ko, S., Sadowski, P., Tull, C., Oh, S.-Y. & Prabhat. Exploring Raw HEP Data using Deep Neural Networks at NERSC. in Proceedings of 38th International Conference on High Energy Physics — PoS(ICHEP2016) (Sissa Medialab, 2017).
    doi
  • Ali, A., Khare, K., Oh, S.-Y. & Rajaratnam, B. Generalized Pseudolikelihood Methods for Inverse Covariance Estimation. in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (eds. Singh, A. & Zhu, J.) vol. 54 280–288 (PMLR, 2017).
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2016

  • Koanantakool, P., Azad, A., Buluc, A., Morozov, D., Oh, S.-Y., Oliker, L. & Yelick, K. Communication-Avoiding Parallel Sparse-Dense Matrix-Matrix Multiplication. in 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS) (Institute of Electrical and Electronics Engineers (IEEE), 2016).
    doi
  • Racah, E., Ko, S., Sadowski, P., Bhimji, W., Tull, C., Oh, S.-Y., Baldi, P. & Prabhat. Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks. in International Conference on Machine Learning and Applications (ICMLA) (2016).
    doi

2014

  • Khare, K., Oh, S.-Y. & Rajaratnam, B. A convex pseudolikelihood framework for high dimensional partial correlation estimation with convergence guarantees. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 77, 803–825 (2014).
  • Oh, S.-Y., Dalal, O., Khare, K. & Rajaratnam, B. Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection. in Advances in Neural Information Processing Systems 27 (eds. Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D. & Weinberger, K. Q.) 667–675 (Curran Associates, Inc., 2014).

2012

  • Levinson, D. F. et al. Genome-Wide Association Study of Multiplex Schizophrenia Pedigrees. American Journal of Psychiatry 169, 963–973 (2012).
    doi

2011

  • Levinson, D. F. et al. Copy Number Variants in Schizophrenia: Confirmation of Five Previous Findings and New Evidence for 3q29 Microdeletions and VIPR2 Duplications. American Journal of Psychiatry 168, 302–316 (2011).
    doi

2001

  • Stompor, R. et al. Making maps of the cosmic microwave background: The MAXIMA example. Physical Review D 65, (2001).
    doi
  • Jaffe, A. H. et al. Cosmology from MAXIMA-1, BOOMERANG, and COBE DMR Cosmic Microwave Background Observations. Physical Review Letters 86, 3475–3479 (2001).
    doi
  • Wu, J. H. P. et al. Asymmetric Beams in Cosmic Microwave Background Anisotropy Experiments. The Astrophysical Journal Supplement Series 132, 1–17 (2001).
    doi

2000

  • Hanany, S. et al. MAXIMA-1: A Measurement of the Cosmic Microwave Background Anisotropy on Angular Scales of 10 arcminutes to 5 degrees. The Astrophysical Journal 545, L5–L9 (2000).
    doi