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Nevin L. Zhang is a professor atThe Hong Kong University of Science and Technology. He is an associate editorof AIJ and IJAR and was an associate editor and advisor of JAIR. He chaired UAI2014 and ECQARU 2003 and served as area chair and SPC for IJCAI, AAAI, UAI andICML.Notable research contributions from his early career include the variableelimination algorithm for Bayesian networks and the incremental pruningalgorithm for POMDPs, both are fundamental to their respective areas. In 2002,he pioneered the research on latent tree models, and since then has beenexploring applications of those models in various areas such as co-occurrencemodeling, latent structure discovery, clustering, topic detection,collaborative filtering, deep learning, density estimation and traditionalChinese medicine. 導(dǎo)讀： Latent tree analysis seeks tomodel the correlations among a set of random variables using a tree of latentvariables. It was proposed as an improvement to latent class analysis — amethod widely used in social sciences and medicine to identify homogeneoussubgroups in a population. It provides new and fruitful perspectives on anumber of machine learning areas, including cluster analysis, topic detection,and deep probabilistic modeling. In this talk, I will give an overview of theresearch on latent tree analysis and various ways it is used in practice.
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