Publications

Probabilistic Modeling and Inference

  • X. Wei, D. Zeng, and J. Yin.
    Multi-label annotation aggregation in crowdsourcing.
    arXiv:1706.06120, 2017.
    [abstract] [pdf]

  • Q. Ho*, J. Yin*, and E. P. Xing. (*joint first authors)
    Latent space inference of Internet-scale networks.
    Journal of Machine Learning Research, 17(78):1−41, 2016.
    [abstract] [pdf]

  • L. Zhu, D. Guo, J. Yin, G. Ver Steeg, and A. Galstyan.
    Scalable temporal latent space inference for link prediction in dynamic social networks.
    IEEE Transactions on Knowledge and Data Engineering, Vol. 28, Iss. 10, 2765−2777, 2016.
    [abstract] [pdf]

  • J. Yin, Q. Ho, and E. P. Xing.
    A scalable approach to probabilistic latent space inference of large-scale networks.
    Advances in Neural Information Processing Systems (NIPS), 2013.
    [abstract] [pdf] [appendix] [poster]

  • Q. Ho, J. Yin, and E. P. Xing.
    On triangular versus edge representations - towards scalable modeling of networks.
    Advances in Neural Information Processing Systems (NIPS), 2012.
    [abstract] [pdf] [appendix] [code]

  • J. Yin, N. Beerenwinkel, J. Rahnenführer, and T. Lengauer.
    Model selection for mixtures of mutagenetic trees.
    Statistical Applications in Genetics and Molecular Biology, Vol. 5, Iss. 1, Article 17, 2006.
    [abstract] [pdf] [code]

High-dimensional Nonparametric Inference

  • J. Yin and Y. Yu.
    Convex-constrained sparse additive modeling and its extensions.
    Uncertainty in Artificial Intelligence (UAI), 2017.
    [abstract] [pdf] [poster]

  • M. Marchetti-Bowick, J. Yin, J. A. Howrylak, and E. P. Xing.
    A time-varying group sparse additive model for genome-wide association studies of dynamic complex traits.
    Bioinformatics, Vol. 32, Iss. 19, 2903−2910, 2016.
    [abstract] [pdf]

Health and Biological Sciences

  • M. Marchetti-Bowick, J. Yin, J. A. Howrylak, and E. P. Xing.
    A time-varying group sparse additive model for genome-wide association studies of dynamic complex traits.
    Bioinformatics, Vol. 32, Iss. 19, 2903−2910, 2016.
    [abstract] [pdf]

  • J. Yin.
    Hypothesis testing of meiotic recombination rates from population genetic data.
    BMC Genetics, 15:122, 2014.
    [abstract] [pdf] [code]

  • E. P. Xing, R. Curtis, G. Schoenherr, S. Lee, J. Yin, K. Puniyani, W. Wu, and P. Kinnaird.
    GWAS in a box: statistical and visual analytics of structured associations via GenAMap.
    PLOS ONE, 9(6): e97524, 2014.
    [abstract] [pdf]

  • R. Curtis, J. Yin, P. Kinnaird, and E. P. Xing.
    Finding genome-transcriptome-phenome association with structured association mapping and visualization in GenAMap.
    Pacific Symposium on Biocomputing (PSB), 2012.
    [abstract] [pdf] [poster]

  • J. Yin, N. Beerenwinkel, J. Rahnenführer, and T. Lengauer.
    Model selection for mixtures of mutagenetic trees.
    Statistical Applications in Genetics and Molecular Biology, Vol. 5, Iss. 1, Article 17, 2006.
    [abstract] [pdf] [code]

Workshop Papers

  • X. Wei, D. Zeng, and J. Yin.
    From multi-class to multi-label: A Bayesian approach to annotation aggregation in crowdsourcing.
    INFORMS Workshop on Data Science, Best Paper Award Runner-up, 2017.

  • J. Yin, Q. Ho, and E. P. Xing.
    Scalable overlapping community detection in Internet-scale networks.
    Workshop on Information Technologies and Systems (WITS), 2015.
    [abstract] [pdf]

  • W. Dai, J. Wei, X. Zheng, J. K. Kim, S. Lee, J. Yin, Q. Ho, and E. P. Xing.
    Petuum: A system for iterative-convergent distributed ML.
    Workshop on Big Learning at Advances in Neural Information Processing Systems (NIPS), 2013.
    [abstract] [pdf]