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Statistical Machine Learning; Bayesian Analysis; Numerical Algebra and Optimization
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Machine Learning: A Probabilistic Perspective. ACM-Class, Spring 2014
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Foundations of Machine Learning. IEEE-Class, Spring 2014
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Zhihua Zhang. The Matrix Ridge Approximation: Algorithms and Applications. Machine Learning , 97: 227-258, 2014.
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Zhihua Zhang, Dakan Wang, Guang Dai, and Michael I. Jordan. Matrix-Variate Dirichlet Process Priors with Applications. Bayesian Analysis, 9:259-286, 2014.
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Shusen Wang and Zhihua Zhang. Improving CUR Matrix Decomposition and the Nystrom Approximation via Adaptive Sampling. Journal of Machine Learning Research (JMLR), 14: 2729-2769, 2013.
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Zhihua Zhang, Dehua Liu, Guang Dai and Michael I. Jordan. Coherence Functions with Applications in Large-Margin Classification Methods. Journal of Machine Learning Research (JMLR), 13: 2705-2734, 2012.
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Zhihua Zhang, Shusen Wang, Dehua Liu and Michael I. Jordan. EP-GIG priors and applications in Bayesian sparse learning. Journal of Machine Learning Research (JMLR), 13: 2031-2061, 2012.
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Zhihua Zhang, G. Dai and M. I. Jordan. Bayesian generalized kernel mixed models.Journal of Machine Learning Research, 12, 31-59, 2011.
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Zhihua Zhang, G. Dai, C. Xu and M. I. Jordan. Regularized Discriminant Analysis, Ridge Regression and Beyond. Journal of Machine Learning Research, 11, 2199-2228, 2010.
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Zhihua Zhang, Cheng Chen, Gung Dai, Wu-Jun Li and Dit-Yan Yeung. Multicategory Large Margin Classification Methods: Hinge Losses vs. Coherence Functions. Artificial Intelligence, 215: 55-78, 2014.
Action Editor of JMLR (Journal of Machine Learning Research)