10 citations · 37 across the 15 of their papers we have counts for
4 papers · 1 filter
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…