The inference of causal relationships is a problem of fundamental interest in science.

To do so, we bring classical optimization arguments for saddle-point algorithms to the geometry of Wasserstein space.

Noisy labels can impair the performance of deep neural networks.

The variability of what "similar" means in this context, which transformations are allowed, and whether data points themselves or their feature representations