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Dr. Tao CHEN

Bsc, School of Mathematical Science, Peking University

PhD, Computer Science Department, The Hong Kong University of Science and Technology

Telephone: 0756-3620858
FAX: 0756-3620888
E-mail: taochen@uic.edu.hk
Room: E408-R8
Postal address: UIC Building, United International College, Zhuhai Campus of Beijing Normal University, Jinfeng Road, Xiangzhou District, Zhuhai,China

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Research Interests

- General: Bayesian network, Probabilistic graphical models, machine learning, and their applications
- Current Focus: Latent variable models (LVMs), latent structure discovery, the application of LVMs to density estimation and clustering, Statistical machine learning.

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Selected Publications

  1. Tao Chen, Nevin L. Zhang, and Yi Wang (2008). Efficient Model Evaluation in the Search-Based Approach to Latent Structure Discovery. In Proceedings of the Fourth European Workshop on Probabilistic Graphical Models (PGM-08), 57-64.
  2. Yi Wang, Nevin L. Zhang, and Tao Chen (2008). Latent tree models and approximate inference in Bayesian networks. Journal of Artificial Intelligence Research (JAIR), 32, 879-900.
  3. Nevin L. Zhang, Shihong Yuan, Tao Chen, and Yi Wang (2008). Statistical validation of traditional Chinese medicine theories. Journal of Alternative and Complementary Medicine, 14(5), 583-587.
  4. Nevin L. Zhang, Shihong Yuan, Tao Chen, and Yi Wang (2008). Latent tree models and diagnosis in traditional Chinese medicine. Artificial Intelligence in Medicine, 42(3), 229-245.
  5. Yi Wang, Nevin L. Zhang, and Tao Chen (2008). Latent tree models and approximate inference in Bayesian networks. In Proceedings of the Twenty-Third Conference on Artificial Intelligence (AAAI-08), 1112-1118.
  6. T. Chen and N. L. Zhang (2006). Quartet-Based Learning of Hierarchical Latent Class Models: Discovery of Shallow Latent Variables. In Proceedings of 9th International Symposium on Artificial Intelligence and Mathematics.
  7. T. Chen, T. Kocka, and N. L. Zhang (2005). Effective Dimensions of Partially Observed Polytrees. International Journal of Approximate Reasoning. 38(3): 311-332.