Tam Le - Publications

(*: equal contribution)

Selected Journals / Conference papers

  • Optimal Transport for Measures with Noisy Tree Metric
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2024. (Acceptance rate: 546/1980)
    Tam Le, Truyen Nguyen, Kenji Fukumizu.
    (Preliminary workshop version in NeurIPS workshop: OTML, 2023)

Others

Other Journals / Conference papers / Preprints

Thesis

Workshop Posters / Talks

Workshop Posters

Invited Talks

  • 2024, Scalable Robust Optimal Transport for Measures with Noisy Tree Metric , Data Science Seminar (Data Descriptive Science), Kyoto, Japan (hybrid). (to present on February 15)

  • 2024, Local Structures for Large-Scale Optimal Transport, The Mathematics of Data program – Workshop on Optimal Transport and PDEs, IMS, NUS, Singapore. [SLIDE]

  • 2023, Sobolev Transport, Data Science Seminar (Data Descriptive Science), Tokyo, Japan. [SLIDE]

  • 2023, Optimal transport for measures on a graph, Statistical Mathematics Seminar Series, Virtual. [SLIDE]

  • 2023, Optimal Transport with Local Structures for Large-Scale Applications, CREST Seminar: Innovation of Deep Structured Models with Representation of Mathematical Intelligence, Virtual. [SLIDE]

  • 2022, Optimal Transport and Its Applications on Machine Learning, Statistical Mathematics Seminar Series, Virtual. [SLIDE]

  • 2022, Optimal Transport and Its Applications on Machine Learning, Optimal Transport Seminar (Data Descriptive Science), Virtual.

  • 2022, Sobolev Transport: A Scalable Metric for Probability Measures on Graphs, Kyoto Machine Learning workshop, Kyoto University, Japan (hybrid).

  • 2022, Geometric Approaches for Persistence Diagrams in Topological Data Analysis, Asia Pacific Seminar on Applied Topology and Geometry (APATG), Virtual. [SLIDE]

  • 2022, On Scalability of Optimal Transport with Tree/Graph metric, AAAI workshop (OT-SDM: International Workshop on Optimal Transport and Structured Data Modeling), Virtual. [SLIDE]

  • 2021, Adversarial Regression with Doubly Non-negative Weighting Matrices, VinAI workshop, Virtual. [SLIDE]

  • 2021, An Introduction on Tree-(Sliced)-Wasserstein Geometry, RIKEN AIP Open Seminar Series, Virtual. [SLIDE]

  • 2019, Kernel methods for Persistence Diagrams: A Geometric Approach, Institute for Advanced Study, Kyoto University, Japan. [SLIDE]

  • 2016, Geometry-Aware Metric Learning for Histograms, MI2I Open Seminar Series, National Institute for Materials Science, Tsukuba, Japan. [SLIDE]

  • 2015, Metric Learning for Histograms, Nagoya Institute of Technology, Japan.

  • 2015, Unsupervised Riemannian Metric Learning for Histograms, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Japan.

  • 2015, Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Embeddings, IST Seminar, Graduate School of Informatics, Kyoto University, Japan. [SLIDE]

  • 2014, Adaptive Euclidean Maps for Histograms: Generalized Aitchison Embeddings, Lear, INRIA Rhone Alpes, Grenoble, France. [SLIDE]