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[ Author(Desc)] Title Type Year
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Nguyen, J. & Zhu, M., 2013. Content-boosted matrix factorization techniques for recommender systems. Statistical Analysis and Data Mining: The ASA Data Science Journal, 6, pp.286–301. Available at: http://doi.org/10.1002/sam.11184.
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Parissenti, A.M. et al., 2015. Tumor RNA disruption predicts survival benefit from breast cancer chemotherapy. Breast Cancer Research and Treatment, 153, pp.135–144. Available at: http://doi.org/10.1007/s10549-015-3498-9.
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Soltan-Ghoraie, L., Burkowski, F. & Zhu, M., 2015. Sparse networks of directly coupled, polymorphic, and functional side chains in allosteric proteins. Proteins: Structure, Function, and Bioinformatics, 83, pp.497–516. Available at: http://doi.org/10.1002/prot.24752.
Soltan-Ghoraie, L. et al., 2014. Residue-specific side-chain polymorphisms via particle belief propagation. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11, pp.33–41. Available at: http://doi.org/10.1109/TCBB.2013.130.
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.
Su, W., Chipman, H.A. & Zhu, M., 2011. Pseudo-likelihood inference underestimates model uncertainty: Evidence from Bayesian nearest neighbours. Journal of the Iranian Statistical Society, 10, pp.167–180. Available at: http://jirss.irstat.ir/article-1-162-en.html.
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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.
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.
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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.
Xin, L. & Zhu, M., 2012. Stochastic stepwise ensembles for variable selection. Journal of Computational and Graphical Statistics, 21, pp.275–294. Available at: http://doi.org/10.1080/10618600.2012.679223.
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Young, S.S., Yuan, F. & Zhu, M., 2012. Chemical descriptors are more important than learning algorithms for modelling. Molecular Informatics, 31, pp.707–710. Available at: http://doi.org/10.1002/minf.201200031.
Yuan, Y., Su, W. & Zhu, M., 2015. Threshold-free measures for assessing the performance of medical screening tests. Frontiers in Public Health, 3, p.57. Available at: http://doi.org/10.3389/fpubh.2015.00057.
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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.
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.
Zhu, M., 2015. Use of majority votes in statistical learning. Wiley Interdisciplinary Reviews: Computational Statistics, 7, pp.357–371. Available at: http://doi.org/10.1002/wics.1362.
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.

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