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Selected Publications

13. Dai Hai Nguyen, Tetsuya Sakurai, Hiroshi Mamitsuka, "Wasserstein gradient flow over Variational parameter space for variational inference", arXiv:2310.16705[cs.LG] [paper] 

12. Dai Hai Nguyen, Tetsuya Sakurai, "Moreau-Yoshida Variational Transport: a general framework for solving regularized distributional optimization problems", Machine Learning 2024, 113, p.6697-6724. [paper] [slide]

11. Dai Hai Nguyen, Tetsuya Sakurai, "Mirror Variational Transport: A Particle-based Algorithm for Distributional Optimization on Constrained Domains", Machine Learning 2023, 112, p.2845-2869. [paper] [slide]

10. Dai Hai Nguyen, Koji Tsuda, "On a linear fused Gromov-Wasserstein distance for graph structured data", Pattern Recognition 2023, 138, p.109351. [paper] 

9. Haishan Zhang, Dai Hai Nguyen, Koji Tsuda, "Differentiable optimization layers enhance GNN-based mitosis detection", Scientific Reports 2023, 13, 14306, [paper] 

8. Dai Hai Nguyen, Koji Tsuda, "Generating reaction trees with cascaded variational autoencoders",  The Journal of Chemical Physics 2022, 156(4),p.044117. [paper ]

7. Dai Hai Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka, "Machine Learning for Metabolic Identification". In: Nishimura K., Murase M., Yoshimura K. (eds) Creative Complex Systems. Creative Economy, Springer, Singapore, 2021. [paper] [slide]

6. Dai Hai Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka., "Learning Subtree Pattern Importance for Weisfeiler- Lehman based Graph Kernels", Machine Learning 2021, 110(7), p.1585-1607.[paper ][slide]

5. Dai Hai Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka, "ADAPTIVE: leArning DAta-dePendenT, concIse molecular VEctors for fast, accurate metabolite identification from tandem mass spectra",  Bioinformatics, (Proceedings of the 27th International Conference on Intelligent Systems for Molecular Biology (ISMB/ECCB 2019), Basel, Switzerland), p.164–172 .[paper][slide]

4. Anh Duc Le,  Dai Hai Nguyen, Bipin Indurkhya, Masaki Nakagawa, "Stroke order normalization for improving recognition of online handwritten mathematical expressions", International Journal on Document Analysis and Recognition (IJDAR 2019), 22(1), p.29–39. [paper]

3. Dai Hai Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka, "Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches", Briefings in Bioinformatics 2018, bby066. [paper][slide]

2. Dai Hai Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka, "SIMPLE: Sparse Interaction Model over Peaks of MoLEcules for Fast, Interpretable Metabolite Identification from Tandem Mass Spectra". Bioinformatics, 34 (13) (Proceedings of the 26th International Conference on Intelligent Systems for Molecular Biology (ISMB 2018), Chicago, USA), p.323–332. [paper][slide]

1. Dai Hai Nguyen, Le Duc Anh, Masaki Nakagawa, "Recognition of online handwritten math symbols using deep neural networks",  IEICE Transactions on Information and Systems 2016, 99 (12), p.3110-3118.