Optimal Transport (OT) has recently emerged as a central tool in data sciences to compare
in a geometrically faithful way point clouds and more generally probability distributions. The …

Multimodal single-cell omics technologies enable multiple molecular programs to be
simultaneously profiled at a global scale in individual cells, creating opportunities to study …

We consider the problem of estimating the optimal transport map between two probability
distributions, $ P $ and $ Q $ in $\mathbb {R}^ d $, on the basis of iid samples. All existing …

Spatially resolved transcriptomics (SRT) technologies measure messenger RNA (mRNA)
expression at thousands of locations in a tissue slice. However, nearly all SRT technologies …

We consider the problem of estimating the optimal transport map between a (fixed) source
distribution $ P $ and an unknown target distribution $ Q $, based on samples from $ Q …

Optimal transport (OT) compares probability distributions by computing a meaningful
alignment between their samples. CO-optimal transport (COOT) takes this comparison …

Computational methods for integrating single-cell transcriptomic data from multiple samples
and conditions do not generally account for imbalances in the cell types measured in …

Optimal transport (OT) has emerged as a powerful framework to compare probability
measures, a fundamental task in many statistical and machine learning problems …

Estimating optimal transport (OT) maps (aka Monge maps) between two measures P and Q
is a problem fraught with computational and statistical challenges. A promising approach …

Gromov-Wasserstein distance has many applications in machine learning due to its ability to
compare measures across metric spaces and its invariance to isometric transformations …