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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.
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Fan, G. & Zhu, M., 2011. Detection of rare items with TARGET. Statistics and Its Interface, 4, pp.11–17. Available at: http://doi.org/10.4310/SII.2011.v4.n1.a2.
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.
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Gu, H., Kenney, T. & Zhu, M., 2010. Partial generalized additive models: An information-theoretic approach for dealing with concurvity and selecting variables. Journal of Computational and Graphical Statistics, 19, pp.531–551. Available at: http://doi.org/10.1198/jcgs.2010.07139.
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Hastie, T.J. & Zhu, M., 2001. Discussion of "Dimension reduction and visualization in discriminant analysis" by Cook and Yin. Australian and New Zealand Journal of Statistics, 43, pp.179–185.
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. 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.
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.
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.
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.
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Kustra, R., Shioda, R. & Zhu, M., 2006. A factor analysis model for functional genomics. BMC Bioinformatics, 7, p.216. Available at: http://doi.org/10.1186/1471-2105-7-216.
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Laflamme-Sanders, A. & Zhu, M., 2008. LAGO on the unit sphere. Neural Networks, 21, pp.1220–1223. Available at: http://doi.org/10.1016/j.neunet.2008.08.002.
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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.

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