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.