Tam Le (RIKEN AIP)
Research interests: Optimal transport, tree-Wasserstein geometry, geometric machine learning, Riemannian manifold, topological data analysis, kernel methods, parametric optimization, metric learning.
Contact
RIKEN AIP (Kyoto University Office)
Artificial Intelligence Research Unit
Graduate School of Informatics, Kyoto University
Yoshida Honmachi, Sakyo-ku, Kyoto, 606-8501, Japan.
Email: tam.le[at]riken.jp or lttam.vn[at]gmail.com
My CV is here - Updated February, 2021.
News
23-Jan-2021, Two papers, entitled “Entropy Partial Transport with Tree Metrics: Theory and Practice” (joint work with Truyen Nguyen) — selected for oral presentation — and “Flow-based Alignment Approaches for Probability Measures in Different Spaces” (joint work with Nhat Ho and Makoto Yamada), have been accepted to AISTATS’21.
23-Jan-2021, Slides for my presentation (An Introduction on Tree-(Sliced)-Wasserstein Geometry) at RIKEN AIP Open Seminar Series, Virtual.
05-Apr-2020, Our proposal has been accepted by the Grants-in-Aid for Young Scientists as PI (04/2020 - 03/2023).
26-Oct-2019, Matlab code for tree-(sliced)-Wasserstein distance in our NeurIPS’19 (Tree-Sliced Variants of Wasserstein Distances) is available [Github].
04-Sep-2019, Our paper, entitled “Tree-Sliced Variants of Wasserstein Distances” has been accepted to NeurIPS’19! This is joint work with Marco Cuturi, Kenji Fukumizu, and Makoto Yamada.
11-Jul-2019, Slides for my presentation (Kernel methods for Persistence Diagrams: A Geometric Approach) at Institute for Advanced Study, Kyoto University.
25-Apr-2019, Python code for our ICML’19 (Safe Grid Search with Optimal Complexity) is available [Github], coded by Eugene Ndiaye.
22-Apr-2019, Our paper, entitled “Safe Grid Search with Optimal Complexity” has been accepted to ICML’19. This is joint work with Eugene Ndiaye, Olivier Fercoq, Joseph Salmon and Ichiro Takeuchi.
19-Oct-2018, Matlab code v0.1 for Persistence Fisher distance (Fisher information metric between two persistence diagrams with or without Fast Gauss Transform) is available [Github].
05-Sep-2018, Our paper, entitled “Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams” has been accepted to NeurIPS’18!
01-Sep-2017, Joined in High-Dimensional Statistical Modeling Team, RIKEN AIP as a postdoctoral researcher.
05-Apr-2017, Our proposal has been accepted by the Grants-in-Aid for Young Scientists (B) as PI (04/2017 - 03/2020).
Research Support
Education
Research Experience
09/2017 - present, Postdoctoral Researcher, High-Dimensional Statistical Modeling Team, RIKEN Center for Advanced Intelligence Project (RIKEN AIP).
02/2016 - 08/2017, Postdoctoral Researcher, Nagoya Institute of Technology & National Institute for Materials Science, Japan.
01/2016, Associate Researcher, Nagoya Institute of Technology, Japan.
10/2015 - 12/2015, Associate Researcher, Kyoto University, Japan.
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