Generalized Aitchison Embeddings for Histograms. - Tam Le; Marco Cuturi (ACML, 2013)

Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations. - Tam Le; Marco Cuturi (ICML, 2015)

Adaptive Euclidean maps for histograms: generalized Aitchison embeddings. - Tam Le; Marco Cuturi (Mach. Learn., 2015)

Design and implementation of a home automation system for smart grid applications. - Abdelhakim Ahmim; Tam Le; Esther Ososanya; Sasan Haghani (ICCE, 2016)

Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams. - Tam Le; Makoto Yamada (NeurIPS, 2018)

CapChain: A Privacy Preserving Access Control Framework Based on Blockchain for Pervasive Environments. - Tam Le; Matt W. Mutka (SMARTCOMP, 2018)

Riemannian Manifold Kernel for Persistence Diagrams. - Tam Le; Makoto Yamada (CoRR, 2018)

Safe Grid Search with Optimal Complexity. - Eugène Ndiaye; Tam Le; Olivier Fercoq; Joseph Salmon; Ichiro Takeuchi (ICML, 2019)

Access control with delegation for smart home applications. - Tam Le; Matt W. Mutka (IoTDI, 2019)

PPCA: privacy-preserving conditional actions for IoT environments using smart contracts. - Tam Le; Matt W. Mutka (MobiQuitous, 2019)

Tree-Sliced Variants of Wasserstein Distances. - Tam Le; Makoto Yamada; Kenji Fukumizu; Marco Cuturi (NeurIPS, 2019)

A Lightweight Block Validation Method for Resource-Constrained IoT Devices in Blockchain-Based Applications. - Tam Le; Matt W. Mutka (WOWMOM, 2019)

Tree-Sliced Approximation of Wasserstein Distances. - Tam Le; Makoto Yamada; Kenji Fukumizu; Marco Cuturi (CoRR, 2019)

LSMI-Sinkhorn: Semi-supervised Squared-Loss Mutual Information Estimation with Optimal Transport. - Yanbin Liu; Makoto Yamada; Yao-Hung Hubert Tsai; Tam Le; Ruslan Salakhutdinov; Yi Yang 001 (CoRR, 2019)

Computationally Efficient Tree Variants of Gromov-Wasserstein. - Tam Le; Nhat Ho; Makoto Yamada (CoRR, 2019)

On Scalable Variant of Wasserstein Barycenter. - Tam Le; Viet Huynh; Nhat Ho; Dinh Q. Phung; Makoto Yamada (CoRR, 2019)

Topological Bayesian Optimization with Persistence Diagrams. - Tatsuya Shiraishi; Tam Le; Hisashi Kashima; Makoto Yamada (ECAI, 2020)

Flow-based Alignment Approaches for Probability Measures in Different Spaces. - Tam Le; Nhat Ho; Makoto Yamada (AISTATS, 2021)

Entropy Partial Transport with Tree Metrics: Theory and Practice. - Tam Le; Truyen Nguyen (AISTATS, 2021)

Point-set Distances for Learning Representations of 3D Point Clouds. - Trung Nguyen; Quang-Hieu Pham; Tam Le; Tung Pham 001; Nhat Ho; Binh-Son Hua (ICCV, 2021)

Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search. - Vu Nguyen; Tam Le; Makoto Yamada; Michael A. Osborne (ICML, 2021)

Nonsmooth Implicit Differentiation for Machine-Learning and Optimization. - Jérôme Bolte; Tam Le; Edouard Pauwels; Antonio Silveti-Falls (NeurIPS, 2021)

Adversarial Regression with Doubly Non-negative Weighting Matrices. - Tam Le; Truyen Nguyen; Makoto Yamada; Jose H. Blanchet; Viet Anh Nguyen (NeurIPS, 2021)

LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with Optimal Transport. - Yanbin Liu; Makoto Yamada; Yao-Hung Hubert Tsai; Tam Le; Ruslan Salakhutdinov; Yi Yang 001 (ECML/PKDD (1), 2021)

Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics. - Tam Le; Truyen Nguyen; Dinh Phung 001; Viet Anh Nguyen (AISTATS, 2022)

Scalable Unbalanced Sobolev Transport for Measures on a Graph. - Tam Le; Truyen Nguyen; Kenji Fukumizu (AISTATS, 2023)

Dynamic Flows on Curved Space Generated by Labeled Data. - Xinru Hua; Truyen Nguyen; Tam Le; Jose H. Blanchet; Viet Anh Nguyen (IJCAI, 2023)

Subgradient Sampling for Nonsmooth Nonconvex Minimization. - Jérôme Bolte; Tam Le; Edouard Pauwels (SIAM J. Optim., 2023)

Optimal Transport for Measures with Noisy Tree Metric. - Tam Le; Truyen Nguyen; Kenji Fukumizu (AISTATS, 2024)

Scalable Counterfactual Distribution Estimation in Multivariate Causal Models. - Thong Pham; Shohei Shimizu; Hideitsu Hino; Tam Le (CLeaR, 2024)

Generalized Sobolev Transport for Probability Measures on a Graph. - Tam Le; Truyen Nguyen; Kenji Fukumizu (ICML, 2024)

Sliced Wasserstein with Random-Path Projecting Directions. - Khai Nguyen; Shujian Zhang; Tam Le; Nhat Ho (ICML, 2024)

Tree-Sliced Wasserstein Distance on a System of Lines. - Viet-Hoang Tran; Trang Pham; Tho Tran; Tam Le; Tan M. Nguyen (CoRR, 2024)

Nonsmooth Nonconvex Stochastic Heavy Ball. - Tam Le (J. Optim. Theory Appl., 2024)

SAVA: Scalable Learning-Agnostic Data Valuation. - Samuel Kessler; Tam Le; Vu Nguyen (ICLR, 2025)

Universal generalization guarantees for Wasserstein distributionally robust models. - Tam Le; Jérôme Malick (ICLR, 2025)

Spherical Tree-Sliced Wasserstein Distance. - Hoang V. Tran; Thanh T. Chu; Minh-Khoi Nguyen-Nhat; Huyen Trang Pham; Tam Le; Tan Minh Nguyen (ICLR, 2025)

Distance-Based Tree-Sliced Wasserstein Distance. - Hoang V. Tran; Minh-Khoi Nguyen-Nhat; Huyen Trang Pham; Thanh T. Chu; Tam Le; Tan Minh Nguyen (ICLR, 2025)

Few-shot object detection via synthetic features with optimal transport. - Anh-Khoa Nguyen Vu; Thanh-Toan Do; Vinh-Tiep Nguyen; Tam Le; Minh-Triet Tran; Tam V. Nguyen 002 (Comput. Vis. Image Underst., 2025)

Scalable Sobolev IPM for Probability Measures on a Graph. - Tam Le; Truyen Nguyen; Hideitsu Hino; Kenji Fukumizu (CoRR, 2025)

Orlicz-Sobolev Transport for Unbalanced Measures on a Graph. - Tam Le; Truyen Nguyen; Hideitsu Hino; Kenji Fukumizu (CoRR, 2025)

Distance-Based Tree-Sliced Wasserstein Distance. - Hoang V. Tran; Khoi N. M. Nguyen; Trang Pham; Thanh T. Chu; Tam Le; Tan M. Nguyen (CoRR, 2025)

Spherical Tree-Sliced Wasserstein Distance. - Viet-Hoang Tran; Thanh T. Chu; Khoi N. M. Nguyen; Trang Pham; Tam Le; Tan M. Nguyen (CoRR, 2025)

Tree-Sliced Wasserstein Distance with Nonlinear Projection. - Thanh Tran; Viet-Hoang Tran; Thanh T. Chu; Trang Pham; Laurent El Ghaoui; Tam Le; Tan M. Nguyen (CoRR, 2025)