We present a novel neural-networks-based algorithm to compute optimal transport maps
and plans for strong and weak transport costs. To justify the usage of neural networks, we …

Counting dense crowds through computer vision technology has attracted widespread
attention. Most crowd counting datasets use point annotations. In this paper, we formulate …

Characterizing statistical properties of solutions of inverse problems is essential for decision
making. Bayesian inversion offers a tractable framework for this purpose, but current …

Optimal transport (OT) measures distances between distributions in a way that depends on
the geometry of the sample space. In light of recent advances in computational OT, OT …

We define a metric—the network Gromov–Wasserstein distance—on weighted, directed
networks that is sensitive to the presence of outliers. In addition to proving its theoretical …

We establish a bridge between spectral clustering and Gromov-Wasserstein Learning
(GWL), a recent optimal transport-based approach to graph partitioning. This connection …

Optimal transport (OT) is a powerful geometric and probabilistic tool for finding
correspondences and measuring similarity between two distributions. Yet, its original …

In many unpaired image domain translation problems, eg, style transfer or super-resolution,
it is important to keep the translated image similar to its respective input image. We propose …

During training, supervised object detection tries to correctly match the predicted bounding
boxes and associated classification scores to the ground truth. This is essential to determine …

This thesis proposes theoretical and numerical contributions to use Entropy-regularized
Optimal Transport (EOT) for machine learning. We introduce Sinkhorn Divergences (SD), a …