Official PyTorch implementation for paper: Sliced Wasserstein with Random-Path Projecting Directions

Code for "Generalized Sobolev Transport for Probability Measures on a Graph", published at ICML 2024 (Authors: Tam Le, Truyen Nguyen, Kenji Fukumizu)

Code for "Optimal Transport for Measures with Noisy Tree Metric", published at AISTATS 2024 (Authors: Tam Le, Truyen Nguyen, Kenji Fukumizu)

Code for "Scalable Counterfactual Distribution Estimation in Multivariate Causal Models", CLeaR 2024

Code for "Scalable Unbalanced Sobolev Transport for Measures on a Graph", published at AISTATS 2023 (Authors: Tam Le, Truyen Nguyen, Kenji Fukumizu)

Matlab code for tree-Wasserstein distance in the paper "Tree-Sliced Variants of Wasserstein Distances", NeurIPS, 2019. (Tam Le, Makoto Yamada, Kenji Fukumizu, Marco Cuturi) --- A valid positive def…

Pen and paper exercises in machine learning

Code for "Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics", published at AISTATS 2022 (Authors: Tam Le, Truyen Nguyen, Dinh Phung, Viet Anh Nguyen)

Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search at ICML2021

Code for: "Adversarial Regression with Doubly Non-Negative Weighting Matrices" -- NeurIPS, 2021. (Tam Le*, Truyen Nguyen*, Makoto Yamada, Jose Blanchet, Viet Anh Nguyen)

Code for ECML/PKDD paper: "LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with Optimal Transport"

A fair regression library (machine learning research)

Learning Autoencoders with Relational Regularization

Gromov-Wasserstein Factorization Models for Graph Clustering (AAAI-20)

A toolbox of Hawkes processes

Code for "Entropy Partial Transport with Tree Metrics: Theory and Practice" -- AISTATS 2021 (Tam Le & Truyen Nguyen)

Code and data for "Flow-based Alignment Approaches for Probability Measures in Different Spaces" -- AISTATS 2021 (Tam Le, Nhat Ho, Makoto Yamada)

Implementations of several fast approximate algorithms for geometric optimal transport (OT)

Code for NeurIPS 2019 paper "Screening Sinkhorn Algorithm for Regularized Optimal Transport"

Gromov-Wasserstein Learning for Graph Matching and Node Embedding

Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching

Kmeans with flow-based tree Gromov-Wasserstein barycenters

Approximation path and optimal selection of regularization hyperparameter for some machine learning problems.

Compute Persistence Fisher distance (Fisher information metric between two persistence diagrams with and without Fast Gauss Transform) --- Algorithm 1 in Tam Le & Makoto Yamada NIPS'18

Approximation path and optimal selection of regularization hyperparameter for some machine learning problems.

Automatically exported from code.google.com/p/dipha

Web site of the Computational Optimal Transport book

Matlab code for generalized Aitchison embeddings (metric learning for histogram data points)