Recently, Optimal Transport has been proposed as a probabilistic framework in Machine
Learning for comparing and manipulating probability distributions. This is rooted in its rich …

Optimal transport distances have found many applications in machine learning for their
capacity to compare non-parametric probability distributions. Yet their algorithmic complexity …

Optimal transport (OT) methods seek a transformation map (or plan) between two probability
measures, such that the transformation has the minimum transportation cost. Such a …

All famous machine learning algorithms that comprise both supervised and semi-supervised
learning work well only under a common assumption: the training and test data follow the …

The word mover's distance (WMD) is a fundamental technique for measuring the similarity of
two documents. As the crux of WMD, it can take advantage of the underlying geometry of the …

We consider the problem of estimating the counterfactual joint distribution of multiple
quantities of interests (eg, outcomes) in a multivariate causal model extended from the …

We study optimal transport (OT) problem for probability measures supported on a tree metric
space. It is known that such OT problem (ie, tree-Wasserstein (TW)) admits a closed-form …

The Learning-to-match (LTM) framework proves to be an effective inverse optimal transport
approach for learning the underlying ground metric between two sources of data, facilitating …

Optimal transport (OT) is a widely used technique for distribution alignment, with
applications throughout the machine learning, graphics, and vision communities. Without …

Training data are usually limited or heterogeneous in many chemical and biological
applications. Existing machine learning models for chemistry and materials science fail to …