10 citations · 37 across the 15 of their papers we have counts for
15 papers
Convergence of empirical subgradients for optimal transport-based objectives
Tam Le
Optimal transport is widely used to learn distributions, enforce distributional constraints, and model uncertainty. In applications, transport losses are often computed from sample…
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…
Few-Shot Object Detection via Synthetic Features with Optimal Transport
Anh-Khoa Nguyen Vu, Thanh-Toan Do, Vinh-Tiep Nguyen +3
Few-shot object detection aims to simultaneously localize and classify the objects in an image with limited training samples. However, most existing few-shot object detection metho…
Scalable Unbalanced Sobolev Transport for Measures on a Graph
Tam Le, Truyen Nguyen, Kenji Fukumizu
Optimal transport (OT) is a popular and powerful tool for comparing probability measures. However, OT suffers a few drawbacks: (i) input measures required to have the same mass, (i…
Dynamic Flows on Curved Space Generated by Labeled Data
Xinru Hua, Truyen Nguyen, Tam Le +2
The scarcity of labeled data is a long-standing challenge for many machine learning tasks. We propose our gradient flow method to leverage the existing dataset (i.e., source) to ge…
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…
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search
Vu Nguyen, Tam Le, Makoto Yamada +1
Neural architecture search (NAS) automates the design of deep neural networks. One of the main challenges in searching complex and non-continuous architectures is to compare the si…
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…