Tam Le - Publications

(*: equal contribution; ⊛: co-last author)

Selected Journals / Conference papers

• Tree-Sliced Wasserstein Distance: A Geometric Perspective
  International Conference on Machine Learning (ICML), 2025. (Acceptance rate: 3260/12107)
  Hoang V. Tran*, Trang Pham*, Tho Tran, Khoi Nguyen, Thanh Chu, Tam Le⊛, Tan M. Nguyen⊛.

• Tree-Sliced Wasserstein Distance with Nonlinear Projection
  International Conference on Machine Learning (ICML), 2025. (Acceptance rate: 3260/12107)
  Thanh Tran*, Hoang V. Tran*, Thanh Chu, Trang Pham, Laurent El Ghaoui⊛, Tam Le⊛, Tan M. Nguyen⊛.

• Scalable Sobolev IPM for Probability Measures on a Graph
  International Conference on Machine Learning (ICML), 2025. (Acceptance rate: 3260/12107)
  Tam Le*, Truyen Nguyen*, Hideitsu Hino, Kenji Fukumizu.

• Distance-Based Tree-Sliced Wasserstein Distance
  International Conference on Learning Representation (ICLR), 2025. (Acceptance rate: 32.08% out of about 11500 submissions)
  Hoang V. Tran*, Khoi Nguyen*, Trang Pham, Thanh Chu, Tam Le⊛, Tan M. Nguyen⊛.

• Spherical Tree-Sliced Wasserstein Distance
  International Conference on Learning Representation (ICLR), 2025. (Acceptance rate: 32.08% out of about 11500 submissions)
  Hoang V. Tran*, Thanh Chu*, Khoi Nguyen, Trang Pham, Tam Le⊛, Tan M. Nguyen⊛.

• SAVA: Scalable Learning-Agnostic Data Valuation
  International Conference on Learning Representation (ICLR), 2025. (Acceptance rate: 32.08% out of about 11500 submissions)
  Samuel Kessler*, Tam Le*, Vu Nguyen.

• Generalized Sobolev Transport for Probability Measures on a Graph
  International Conference on Machine Learning (ICML), 2024. (Acceptance rate: 2609/9473)
  Tam Le*, Truyen Nguyen*, Kenji Fukumizu.

• Sliced Wasserstein with Random-Path Projecting Directions
  International Conference on Machine Learning (ICML), 2024. (Acceptance rate: 2609/9473)
  Khai Nguyen, Shujian Zhang, Tam Le, Nhat Ho.

• 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)

• Scalable Unbalanced Sobolev Transport for Measures on a Graph .
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2023. (Acceptance rate: 496/1686)
  Tam Le*, Truyen Nguyen*, Kenji Fukumizu.

• Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics . [Video/Supplementary/POSTER]
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2022. (Acceptance rate: 492/1685)
  Tam Le*, Truyen Nguyen*, Dinh Phung, Viet Anh Nguyen.

• Adversarial Regression with Doubly Non-negative Weighting Matrices. [Supplementary/SLIDE/POSTER]
  Neural Information Processing Systems (NeurIPS/NIPS), 2021. (Acceptance rate: 2334/9122)
  Tam Le*, Truyen Nguyen*, Makoto Yamada, Jose Blanchet, Viet Anh Nguyen.

• Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search. [Supplementary/POSTER]
  International Conference on Machine Learning (ICML), 2021. (Acceptance rate: 1184/5513)
  Vu Nguyen*, Tam Le*, Makoto Yamada, Michael A. Osborne.

• Entropy Partial Transport with Tree Metrics: Theory and Practice. [Video/Supplementary/SLIDE/POSTER]
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2021. (Oral rate: 48/1527=3.1%)
  Tam Le*, Truyen Nguyen*
  (This paper was selected for oral presentation. Oral : acceptance : submissions = 48 : 455 : 1527)

• Flow-based Alignment Approaches for Probability Measures in Different Spaces . [Video/Supplementary/POSTER]
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2021. (Acceptance rate: 455/1527)
  Tam Le*, Nhat Ho*, Makoto Yamada.

• Tree-Sliced Variants of Wasserstein Distances. [Supplementary/POSTER]
  Neural Information Processing Systems (NeurIPS/NIPS), 2019. (Acceptance rate: 1428/6743)
  Tam Le, Makoto Yamada, Kenji Fukumizu, Marco Cuturi.

• Safe Grid Search with Optimal Complexity. [Supplementary/POSTER]
  International Conference on Machine Learning (ICML), 2019. (Acceptance rate: 773/3424)
  Eugene Ndiaye, Tam Le, Olivier Fercoq, Joseph Salmon, Ichiro Takeuchi.

• Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams. [Supplementary/POSTER]
  Neural Information Processing Systems (NeurIPS/NIPS), 2018. (Acceptance rate: 1011/4856)
  Tam Le, Makoto Yamada.
  (Preliminary workshop version in ICML workshop: GML, 2018)

• Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations. [Video/Supplemental/SLIDE/POSTER]
  International Conference on Machine Learning (ICML), 2015. (Acceptance rate: 270/1037)
  Tam Le, Marco Cuturi.

• Adaptive Euclidean Maps for Histograms: Generalized Aitchison Embeddings
  Machine Learning Journal (MLJ), 2014.
  Tam Le, Marco Cuturi.
  (Preliminary conference version in ACML, 2013)

Others

Other Journals / Conference papers / Preprints

• Tree-Sliced Entropy Partial Transport
  Under review.
  Hoang V. Tran, Thanh Tran, Thanh Chu, Tam Le⊛, Tan M. Nguyen⊛.

• Revisiting Tree-Sliced Wasserstein Distance Through the Lens of the Fermat–Weber Problem
  Under review.
  Hoang V. Tran, Thanh Chu, Thanh Tran, Khang Tran, Tam Le⊛, Tan M. Nguyen⊛.

• Orlicz-Sobolev Transport for Unbalanced Measures on a Graph
  ArXiv, 2025.
  Tam Le*, Truyen Nguyen*, Hideitsu Hino, Kenji Fukumizu.

• Few-Shot Object Detection via Synthetic Features with Optimal Transport
  Computer Vision and Image Understanding (CVIU) journal, 2025.
  Anh Khoa Nguyen Vu, Thanh-Toan Do, Vinh-Tiep Nguyen, Tam Le, Minh-Triet Tran, Tam V. Nguyen

• Scalable Counterfactual Distribution Estimation in Multivariate Causal Models
  Conference on Causal Learning and Reasoning (CLeaR), 2024.
  Thong Pham, Shohei Shimizu, Hideitsu Hino, Tam Le.

• Dynamic Flows on Curved Space Generated by Labeled Data
  International Joint Conference on Artificial Intelligence (IJCAI), 2023. (Acceptance rate: 15% out of 4566 submissions)
  Xinru Hua, Truyen Nguyen, Tam Le, Jose Blanchet, Viet Anh Nguyen

• Point-set Distances for Learning Representations of 3D Point Clouds
  International Conference on Computer Vision (ICCV), 2021. (Acceptance rate: 1617/6236)
  Trung Nguyen, Quang-Hieu Pham, Tam Le, Tung Pham, Nhat Ho, Binh-Son Hua.

• LSMI-Sinkhorn: Semi-supervised Squared-Loss Mutual Information Estimation with Optimal Transport
  European Conference on Machine Learning and Principles & Practice of Knowledge Discovery in Databases (ECML-PKDD), 2021. (Acceptance rate: 147/685)
  Yanbin Liu*, Makoto Yamada*, Yao-Hung Hubert Tsai, Tam Le, Ruslan Salakhutdinov, Yi Yang

• Topological Bayesian Optimization with Persistence Diagrams
  European Conference on Artificial Intelligence (ECAI), 2020. (Acceptance rate: 365/1363)
  Tatsuya Shiraishi, Tam Le, Hisashi Kashima, Makoto Yamada

• Tree-Wasserstein Barycenter for Large-Scale Multilevel Clustering and Scalable Bayes
  ArXiv, 2019.
  Tam Le*, Viet Huynh*, Nhat Ho*, Dinh Phung, Makoto Yamada

• A General Representation Scheme for Crystalline Solids based on Voronoi-Tessellation Real Feature Values and Atomic Property Data
  Science and Technology of Advance Materials Journal (STAM), 2018.
  Randy Jalem, Masanobu Nakayama, Yusuke Noda, Tam Le, Ichiro Takeuchi, Yoshitaka Tateyama, Hisatsugu Yamazaki

• Generalized Aitchison Embeddings for Histograms. [SLIDE]
  Asian Conference on Machine Learning (ACML), 2013 (oral). (Oral paper rate: 9.7%)
  Tam Le, Marco Cuturi

• Image Categorization Using Hierarchical Spatial Matching Kernel
  Journal of the Institute of Image Electronics Engineers of Japan (IIEEJ), Vol. 42, No. 2, 2013.
  Tam Le, Yousun Kang, Akihiro Sugimoto
  (Preliminary conference version in ICIAR, 2011)

• Hierarchical Spatial Matching Kernel for Image Categorization. [SLIDE]
  International Conference on Image Processing and Recognition (ICIAR), 2011 (oral). (Oral paper rate: 25%)
  Tam Le, Yousun Kang, Akihiro Sugimoto, Son Tran, Thuc Nguyen

• Realtime Traffic Sign Detection Using Color and Shape-Based Features
  Asian Conference on Intelligent Information and Database Systems (ACIIDS), 2010 (oral).
  Tam Le, Son Tran, Seichii Mita, Thuc Nguyen

Thesis

• Geometry-Aware Learning Algorithms for Histogram Data Using Adaptive Metric Embeddings and Kernel Functions
  PhD thesis, Kyoto University, Japan, 2015.

Invited Talks

• 2025, Foundations and Frontiers: Interdisciplinary Perspectives on Mathematical Optimization workshop, RIKEN AIP, Japan.

• 2024, Statistical Mathematics Seminar Series, Virtual. [SLIDE]

• 2024, Data Science Seminar (Data Descriptive Science), Kyoto, Japan (hybrid).

• 2024, The Mathematics of Data program – Workshop on Optimal Transport and PDEs, IMS, NUS, Singapore. [SLIDE]

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

• 2023, Statistical Mathematics Seminar Series, Virtual. [SLIDE]

• 2023, CREST Seminar: Innovation of Deep Structured Models with Representation of Mathematical Intelligence, Virtual. [SLIDE]

• 2022, Statistical Mathematics Seminar Series, Virtual. [SLIDE]

• 2022, Optimal Transport Seminar (Data Descriptive Science), Virtual.

• 2022, Kyoto Machine Learning workshop, Kyoto University, Japan (hybrid).

• 2022, Asia Pacific Seminar on Applied Topology and Geometry (APATG), Virtual. [SLIDE]

• 2022, AAAI workshop - OT-SDM: International Workshop on Optimal Transport and Structured Data Modeling, Virtual. [SLIDE]

• 2021, VinAI workshop, Virtual. [SLIDE]

• 2021, RIKEN AIP Open Seminar Series, Virtual. [SLIDE]

• 2019, Institute for Advanced Study, Kyoto University, Japan. [SLIDE]

• 2016, MI2I Open Seminar Series, National Institute for Materials Science, Tsukuba, Japan. [SLIDE]

• 2015, Nagoya Institute of Technology, Japan.

• 2015, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Japan.

• 2015, IST Seminar, Graduate School of Informatics, Kyoto University, Japan. [SLIDE]

• 2014, Lear, INRIA Rhone Alpes, Grenoble, France. [SLIDE]

Upcoming Invited Talks

• 06/2025, Statistical Mathematics Seminar Series, Virtual.

• 09/2025, Workshop on Optimization and Machine Learning for Data Science and Future Computing, Japan.