4 citations · 10 across the 4 of their papers we have counts for
7 papers
Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics
Tam Le, Truyen Nguyen, Dinh Phung +1
Optimal transport (OT) is a popular measure to compare probability distributions. However, OT suffers a few drawbacks such as (i) a high complexity for computation, (ii) indefinite…
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…
Point-set Distances for Learning Representations of 3D Point Clouds
Trung Nguyen, Quang-Hieu Pham, Tam Le +3
Learning an effective representation of 3D point clouds requires a good metric to measure the discrepancy between two 3D point sets, which is non-trivial due to their irregularity.…
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…