Recent advances in optimal transport for machine learning
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 …
Learning for comparing and manipulating probability distributions. This is rooted in its rich …
Unbalanced minibatch optimal transport; applications to domain adaptation
Optimal transport distances have found many applications in machine learning for their
capacity to compare non-parametric probability distributions. Yet their algorithmic complexity …
capacity to compare non-parametric probability distributions. Yet their algorithmic complexity …
Projection‐based techniques for high‐dimensional optimal transport problems
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 …
measures, such that the transformation has the minimum transportation cost. Such a …
A survey on domain adaptation theory: learning bounds and theoretical guarantees
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 …
learning work well only under a common assumption: the training and test data follow the …
Re-evaluating word mover's distance
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 …
two documents. As the crux of WMD, it can take advantage of the underlying geometry of the …
Scalable counterfactual distribution estimation in multivariate causal models
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 …
quantities of interests (eg, outcomes) in a multivariate causal model extended from the …
Optimal transport for measures with noisy tree metric
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 …
space. It is known that such OT problem (ie, tree-Wasserstein (TW)) admits a closed-form …
Revisiting deep audio-text retrieval through the lens of transportation
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 …
approach for learning the underlying ground metric between two sources of data, facilitating …
[HTML][HTML] Making transport more robust and interpretable by moving data through a small number of anchor points
Optimal transport (OT) is a widely used technique for distribution alignment, with
applications throughout the machine learning, graphics, and vision communities. Without …
applications throughout the machine learning, graphics, and vision communities. Without …
Improving molecular representation learning with metric learning-enhanced optimal transport
Training data are usually limited or heterogeneous in many chemical and biological
applications. Existing machine learning models for chemistry and materials science fail to …
applications. Existing machine learning models for chemistry and materials science fail to …