KI Seminar (online)
The Kantorovich Initiative Seminar takes place online. To attend, please register for the KI mailing list.
Caroline Moosmueller (University of North Carolina at Chapel Hill)
Detecting differences and building classifiers between distributions, given only finite samples, are important tasks in a number of …
Nabarun Deb (KI Postdoc, University of British Columbia)
The Wasserstein distance is a powerful tool in modern
machine learning to metrize the space of probability distributions in a way
that …
Bamdad Hosseini (University of Washington)
Generative models such as Generative Adversarial Nets (GANs), Variational Autoencoders and Normalizing Flows have been very successful …
Jan-Christian Hutter (Genentech)
The theory of optimal transport (OT) gives rise to distance measures between probability distributions that take the geometry of the …
Guillaume Carlier (CEREMADE, Universite Paris Dauphine)
Entropic optimal transport has received a lot of attention in recent years and
has become a popular framework for computational optimal …