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Author Title Type [ Year(Desc)]
2011
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.
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.
Zhu, M. & Fan, G., 2011. Variable selection by ensembles for the Cox model. Journal of Statistical Computation and Simulation, 81, pp.1983–1992. Available at: http://doi.org/10.1080/00949655.2010.511622.
2012
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.
Zhu, M., Wang, S. & Xin, L., 2012. On individual neutrality and collective decision making. The Mathematical Scientist, 37, pp.141–146. Available at: http://www.appliedprobability.org/data/files/TMS%20articles/37_2_8.pdf.
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.
2013
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.
2014
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.
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.
2015
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.
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.
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.
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.
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.
2016
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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