Publications

  • On sparse Gaussian chain graph models
    C. McCarter, S. Kim. Advances in Neural Information Processing Systems (NIPS), 2014.

  • Learning gene networks under SNP perturbations using eQTL datasets
    L. Zhang, S. Kim. PLoS Computational Biology, 2014. [link] [software]

  • A* lasso for learning a sparse Bayesian network structure
    J. Xiang, S. Kim. Advances in Neural Information Processing Systems (NIPS), 2013. [pdf]

  • On high-dimensional sparse structured input-output models with applications to genome-phenome association analysis of complex diseases
    M. Kolar, S. Kim, X. Chen, S. Lee, E. and P. Xing. In I. Rish, G. Cecchi, and A. Lozano, editors. Optimization for Machine Learning, MIT Press. (in press)

  • Structured association analysis leads to insight into Saccharomyces cerevisiae gene regulation by finding multiple contributing eQTL hotspots associated with functional gene modules
    R.E. Curtis, S. Kim, J.L. Woolford, W.Xu, and E. P. Xing. BMC Genomics, 2013. [pdf]

  • Joint estimation of structured sparsity and output structure in multiple-output regression via inverse-covariance regularization
    K.A. Sohn, S. Kim. Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS), 2012. [pdf]

  • Tree-guided group lasso for multi-response regression with structured sparsity, with an application to eQTL mapping
    S. Kim, E. P. Xing. Annals of Applied Statistics, 6(3):1095-1117, 2012. [pdf] [software]

  • Smoothing proximal gradient method for general structured sparse regression
    X. Chen, Q. Lin, S. Kim, J.G. Carbonell, E.P. Xing. Annals of Applied Statistics, 6(2):719-752, 2012. [pre-print] [software]

  • Exploiting genome structure in association analysis
    S. Kim, E. P. Xing. Journal of Computational Biology, 2011. [link]

  • Smoothing proximal gradient method for general structured sparse learning
    X. Chen, Q. Lin, S. Kim, J. Carbonell, E.P. Xing. Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI), 2011. [pdf] Earlier versions are available as:

    • An efficient proximal-gradient method for single and multi-task regression with structured sparsity
      Xi Chen, Qihang Lin, Seyoung Kim, Javier Pena, Jaime G. Carbonell, Eric P. Xing. Manuscript, arXiv:1005.4717. [pdf]

    • Graph-structured multi-task regression and an efficient optimization method for general fused lasso
      Xi Chen, Seyoung Kim, Qihang Lin, Jaime G. Carbonell, Eric P. Xing. Manuscript, arXiv:1005.3579. [pdf]

  • A Bayesian mixture approach to modeling spatial activation patterns in multi-site fMRI data
    S. Kim, P. Smyth, H. Stern. IEEE Transactions on Medical Imaging, 29(6):1260-1274, 2010. [pdf]

  • Tree-guided group lasso for multi-task regression with structured sparsity
    S. Kim, E. P. Xing. Proceedings of the 27th International Conference on Machine Learning (ICML), 2010. [pdf] [software]

  • Multi-population GWA mapping via multi-task regularized regression
    K. Puniyani, S. Kim, E. P. Xing. Proceedings of the 18th International Conference on Intelligence Systems for Molecular Biology (ISMB), 2010. [pdf]

  • Heterogeneous multitask learning with joint sparsity constraints
    X. Yang, S. Kim and E. P. Xing. Advances in Neural Information Processing Systems (NIPS), 2009. [pdf]

  • Statistical estimation of correlated genome associations to a quantitative trait network
    S. Kim, E. P. Xing. PLoS Genetics 5(8): e1000587, 2009. [link] [software]

  • A multivariate regression approach to association analysis of a quantitative trait network
    S. Kim, K. Sohn, E. P. Xing. Proceedings of the 17th Conference on Intelligent Systems for Molecular Biology (ISMB), 2009. [pdf]

  • Feature selection via block-regularized regression
    S. Kim, E. P. Xing. Proceedings of the 24th Conference on Uncertainty in AI (UAI), 2008. [pdf]

  • Test-retest and between-site reliability in a multicenter fMRI study
    L. Friedman, H. Stern, G. Brown, D. Mathalon, J. Turner , G. Glover, R. Gollub, J. Lauriello, K. Lim, T. Cannon, D. Greve, H. Bockholt, A. Belger, B. Mueller, M. Doty, J. He, W. Wells, P. Smyth, S. Pieper, S. Kim, M. Kubicki, M. Vangel, and S. Potkin. Human Brain Mapping, 29(8):958-972, 2008. [pdf]

  • Hierarchical Dirichlet processes with random effects
    S. Kim, P. Smyth. Advances in Neural Information Processing Systems (NIPS), 2006. [pdf]

  • A nonparametric Bayesian approach to detecting spatial activation patterns in fMRI data
    S. Kim, P. Smyth, H. Stern. Proceedings of the 9th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2006. [pdf]

  • Segmental hidden Markov models with random effects for waveform modeling
    S. Kim, P. Smyth. Journal of Machine Learning Research, 7(Jun):945-969, 2006. [pdf]

  • Parametric response surface models for analysis of multi-site fMRI data
    S. Kim, P. Smyth, H. Stern, J. Turner. Proceedings of the 8th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2005. [pdf]

  • Variance component analysis of a multi-site fMRI study
    S. Kim, H. Stern, P. Smyth. Technical Report UCI-TR 04-14, 2004.

  • Modeling waveform shapes with random effects segmental hidden Markov models
    S. Kim, P. Smyth, S. Luther. Proceedings of the 20th Conference on Uncertainty in AI (UAI), 2004. [pdf] (longer version as Technical Report UCI-ICS 04-05, March 2004. [pdf])

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