Publications

See Google Scholar or DBLP for more details.


Conferences:

  • Trong Dinh Thac Do, Longbing Cao. "Gamma-Poisson Dynamic Matrix Factorization Embedded with Metadata Influence." (NIPS-2018) Montreal, CA.

  • Trong Dinh Thac Do, Longbing Cao. "Metadata-dependent Infinite Poisson Factorization for Efficiently Modelling Sparse and Large Matrices in Recommendation." (IJCAI-2018) Stockholm, Sweden.

  • Trong Dinh Thac Do, Longbing Cao. "Coupled Poisson Factorization Integrated with User/Item Metadata for Modeling Popular and Sparse Ratings in Scalable Recommendation." (AAAI-2018) New Orleans, US.

  • Trong Dinh Thac Do, Anne Laurent, and Alexandre Termier. "PGLCM: Efficient parallel mining of closed frequent gradual itemsets." (ICDM-2010), Sydney, AU.


Journals:

  • Qing Liu, Trong Dinh Thac Do, Longbing Cao. "Answer Keyword Generation for Community Question Answering by Multi-aspect Gamma-Poisson Matrix Completion." IEEE Intelligent Systems (2020).

  • Trong Dinh Thac Do, Alexandre Termier, Anne Laurent, Benjamin Negrevergne, Behrooz Omidvar-Tehrani, and Sihem Amer-Yahia. "PGLCM: efficient parallel mining of closed frequent gradual itemsets." Knowledge and Information Systems (KAIS) 43.3 (2015): 497-527.


Will be appeared:

  • Trong Dinh Thac Do, Longbing Cao. "Coupled Attributes-dependent Mondrian Process for Both Static and Dynamic Infinite Relational Learning." Under revision Machine Learning Journal (MLJ).

  • Trong Dinh Thac Do, Longbing Cao. "Deep Recurrent Statistical Models Embedded with Dropout for Dynamic Count Vectors and Interaction Matrices." Submitted to Journal of Machine Learning Research (JMLR)

  • Trong Dinh Thac Do, Longbing Cao. "Group-based Gamma-Poisson Model: Integrating Multi-source Large and Sparse Data for Scalable Recommendation." Under revision Machine Learning Journal (MLJ).

  • Trong Dinh Thac Do, Longbing Cao. "Bayesian Nonparametric Metadata-integrated Coupled Poisson Factorization for Scalable Recommendations."

  • Trong Dinh Thac Do, Longbing Cao. "HDIM: A Heterogeneous Data-driven Infinite Model for Learning Hierarchical Relations in Time-varying Attributed Networks."

  • Trong Dinh Thac Do, Longbing Cao. "Nonparametric Relational Model with Hierarchical Node-to-Community Interactions."


Conference Tutorials:

  • Trong Dinh Thac Do, Longbing Cao and Jinjin Guo. "Statistical Machine Learning: Big, Multi-source and Sparse Data with Complex Relations and Dynamics." In: Thirty-Forth AAAI Conference on Artificial Intelligence (AAAI 2020), New York, USA.

  • Longbing Cao, Trong Dinh Thac Do and Chengzhang Zhu. "Non-IID Learning of Complex Data and Behaviors." In: Twenty-Eight International Joint Conference on Artificial Intelligence (IJCAI 2019), Macao, China.

  • Trong Dinh Thac Do and Longbing Cao. "Statistical Machine Learning of Large, Sparse and Multi-source Data." In: Twenty-Third Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019), Macao, China.


Thesis:

  • Trong Dinh Thac Do. "Non-IID Latent Variable Models." Thesis for Doctor of Philosophy (Ph.D.) in Machine Learning at University of Technology Sydney, Australia, 2019.

  • Trong Dinh Thac Do. "Parallel Mining of Closed Frequent Gradual Patterns." Thesis for Master of Science (M.Sc.) in Data Mining at Universite Grenoble Alpe and Grenoble INP, France, 2011.