Publications

*: student under my supervision
: joint first authors

Probabilistic Modeling and Inference

• J. Yin, Y. Feng, and Y. Liu.
  Modeling behavioral dynamics in digital content consumption: An attention-based neural point process approach with applications in video games.
  Marketing Science, forthcoming.
  [abstract] [pdf]

• Z. Wang, L. Guo, J. Yin, and S. Li.
  Bandit Learning in Many-to-One Matching Markets.
  ACM International Conference on Information and Knowledge Management (CIKM), 2022.
  

• F. Kong, J. Yin, and S. Li.
  Thompson sampling for bandit learning in matching markets.
  International Joint Conference on Artificial Intelligence (IJCAI), 2022 (acceptance rate: 15%).
  [abstract] [pdf]
  

• J. Yin, J. Luo*, and S. A. Brown
  Learning from crowdsourced multi-labeling: A variational Bayesian approach.
  Information Systems Research, 32(3), 752–773, 2021.
  [abstract] [pdf]
  Amazon AWS Machine Learning Research Award, 2019.
  Best Paper Award, Workshop on Information Technologies and Systems, 2018.
  Best Paper Award Runner-up, INFORMS Workshop on Data Science, 2017.
  

• X. Wang* and J. Yin.
  Relaxed multivariate Bernoulli distribution and its applications to deep generative models.
  Uncertainty in Artificial Intelligence (UAI), 2020.
  [abstract] [pdf]

• W. Li, J. Yin, and H. Chen.
  Supervised topic modeling using hierarchical Dirichlet process-based inverse regression: Experiments on e-commerce applications.
  IEEE Transactions on Knowledge and Data Engineering, 30(6), 1192-1205, 2018.
  [abstract] [pdf]

• Q. Ho, J. Yin, and E. P. Xing.
  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, 28(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, M. I. Jordan, and Y. S. Song.
  Joint estimation of gene conversion rates and mean conversion tract lengths from population SNP data.
  Bioinformatics, 25(12), i231−i239, 2009.
  [abstract] [pdf] [code] [cited by three textbooks of evolutionary biology]

• J. Yin, N. Beerenwinkel, J. Rahnenführer, and T. Lengauer.
  Model selection for mixtures of mutagenetic trees.
  Statistical Applications in Genetics and Molecular Biology, 5(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, 32(19), 2903−2910, 2016.
  [abstract] [pdf]

• J. Yin, X. Chen, and E. P. Xing.
  Group sparse additive models.
  International Conference on Machine Learning (ICML), 2012.
  [abstract] [pdf] [poster] [code] [video]

Healthcare 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, 32(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, M. I. Jordan, and Y. S. Song.
  Joint estimation of gene conversion rates and mean conversion tract lengths from population SNP data.
  Bioinformatics, 25(12), i231−i239, 2009.
  [abstract] [pdf] [code] [cited by two textbooks of evolutionary biology]

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

Workshop Papers

• 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]