10 citations · 37 across the 15 of their papers we have counts for
8 papers · 1 filter
Generalized Sobolev Transport for Probability Measures on a Graph
Tam Le, Truyen Nguyen, Kenji Fukumizu
We study the optimal transport (OT) problem for measures supported on a graph metric space. Recently, Le et al. (2022) leverage the graph structure and propose a variant of OT, nam…
Optimal Transport for Measures with Noisy Tree Metric
Tam Le, Truyen Nguyen, Kenji Fukumizu
We study optimal transport (OT) problem for probability measures supported on a tree metric space. It is known that such OT problem (i.e., tree-Wasserstein (TW)) admits a closed-fo…
Adversarial Regression with Doubly Non-negative Weighting Matrices
Tam Le, Truyen Nguyen, Makoto Yamada +2
Many machine learning tasks that involve predicting an output response can be solved by training a weighted regression model. Unfortunately, the predictive power of this type of mo…
Entropy Partial Transport with Tree Metrics: Theory and Practice
Tam Le, Truyen Nguyen
Optimal transport (OT) theory provides powerful tools to compare probability measures. However, OT is limited to nonnegative measures having the same mass, and suffers serious draw…
Tree-Wasserstein Barycenter for Large-Scale Multilevel Clustering and Scalable Bayes
Tam Le, Viet Huynh, Nhat Ho +2
We study in this paper a variant of Wasserstein barycenter problem, which we refer to as tree-Wasserstein barycenter, by leveraging a specific class of ground metrics, namely tree…
Flow-based Alignment Approaches for Probability Measures in Different Spaces
Tam Le, Nhat Ho, Makoto Yamada
Gromov-Wasserstein (GW) is a powerful tool to compare probability measures whose supports are in different metric spaces. GW suffers however from a computational drawback since it…
LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with Optimal Transport
Yanbin Liu, Makoto Yamada, Yao-Hung Hubert Tsai +3
Estimating mutual information is an important statistics and machine learning problem. To estimate the mutual information from data, a common practice is preparing a set of paired…
Tree-Sliced Variants of Wasserstein Distances
Tam Le, Makoto Yamada, Kenji Fukumizu +1
Optimal transport (\OT) theory defines a powerful set of tools to compare probability distributions. \OT~suffers however from a few drawbacks, computational and statistical, which…