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Author Title [ Type(Desc)] Year
Book
Book Chapter
Cheng, L. & Zhu, M., 2021. First-order correction of statistical significance for screening two-way epistatic interactions. In Epistasis: Methods and Protocols. Springer, pp. 181–190. Available at: http://doi.org/10.1007/978-1-0716-0947-7_12.
Zhu, M. et al., 2014. Using machine learning to plan rehabilitation for home care clients: Beyond "black-box" predictions. In Machine Learning in Healthcare Informatics. Springer, pp. 181–207. Available at: http://doi.org/10.1007/978-3-642-40017-9_9.
Zhu, M., 2014. Making personalized recommendations in e-commerce. In Statistics in Action: A Canadian Outlook. Chapman & Hall, pp. 259–268. Available at: http://ssc.ca/sites/default/files/data/Members/public/Publications/BookFiles/Book/259-268.pdf.
Conference Paper
Hofert, M., Prasad, A. & Zhu, M., 2022. Applications of multivariate quasi-random sampling with neural networks. In Monte Carlo and Quasi-Monte Carlo Methods, MCQMC 2020. Springer, pp. 273–289. Available at: https://doi.org/10.1007/978-3-030-98319-2_14.
Zhu, M., 2019. On fitting complex models to noisy data. In Proceedings of the International Conference on Statistics: Theory and Applications. pp. 34.1–34.7. Available at: http://doi.org/10.11159/icsta19.34.
Murdoch, W.J. & Zhu, M., 2016. Expanded alternating optimization for matrix factorization and penalized regression. In Proceedings of the 22nd International Conference on Computational Statistics. pp. 217–229. Available at: http://www.compstat2016.org/docs/COMPSTAT2016_proceedings.pdf?20160807205859.
Su, W., Yuan, Y. & Zhu, M., 2015. A relationship between the average precision and the area under the ROC curve. In Proceedings of the 2015 International Conference on The Theory of Information Retrieval. pp. 349–352. Available at: http://doi.org/10.1145/2808194.2809481.
Forbes, P. & Zhu, M., 2011. Content-boosted matrix factorization for recommender systems: Experiments with recipe recommendation. In Proceedings of the 5th ACM Conference on Recommender Systems. pp. 261–264. Available at: http://doi.org/10.1145/2043932.2043979.
Hoshino, R., Oldford, R.W. & Zhu, M., 2010. Two-stage approach for unbalanced classification with time-varying decision boundary: Application to marine container inspection. In Proceedings of the ACM SIGKDD Workshop on Intelligence and Security Informatics. pp. 1:1–1:5. Available at: http://doi.org/10.1145/1938606.1938607.
Journal Article
Hofert, M., Prasad, A. & Zhu, M., Accepted. Dependence model assessment and selection with DecoupleNets. Journal of Computational and Graphical Statistics. Available at: https://doi.org/10.1080/10618600.2022.2157835.
Hofert, M., Prasad, A. & Zhu, M., Accepted. RafterNet: Probabilistic predictions in multi-response regression. The American Statistician. Available at: https://doi.org/10.1080/00031305.2022.2141857.
Hofert, M., Prasad, A. & Zhu, M., 2022. Multivariate time-series modeling with generative neural networks. Econometrics and Statistics, 23, pp.147–164. Available at: https://doi.org/10.1016/j.ecosta.2021.10.011.
Hofert, M., Prasad, A. & Zhu, M., 2021. Quasi-random sampling for multivariate distributions via generative neural networks. Journal of Computational and Graphical Statistics, 30, pp.647–670. Available at: http://doi.org/10.1080/10618600.2020.1868302.
Wu, Y., Qin, Y. & Zhu, M., 2020. High-dimensional covariance matrix estimation using a low-rank and diagonal decomposition. Canadian Journal of Statistics, 48, pp.308–337. Available at: http://doi.org/10.1002/cjs.11532.
Hofert, M. et al., 2019. A framework for measuring association of random vectors via collapsed random variables. Journal of Multivariate Analysis, 172, pp.5–27. Available at: http://doi.org/10.1016/j.jmva.2019.02.012.
Zhang, C., Wu, Y. & Zhu, M., 2019. Pruning variable selection ensembles. Statistical Analysis and Data Mining: The ASA Data Science Journal, 12, pp.168–184. Available at: http://doi.org/10.1002/sam.11410.
Wu, Y., Qin, Y. & Zhu, M., 2019. Quadratic discriminant analysis for high-dimensional data. Statistica Sinica, 29, pp.939–960. Available at: http://doi.org/10.5705/ss.202016.0034.
Xin, L., Zhu, M. & Chipman, H.A., 2017. A continuous-time stochastic block model for basketball networks. Annals of Applied Statistics, 11, pp.553–597. Available at: http://doi.org/10.1214/16-AOAS993.

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