The Wasserstein distance is a powerful tool in modern
machine learning to metrize the space of probability distributions in a way
that takes into account the geometry of the domain.
Therefore, a lot of attention has been devoted in the literature to …
Wasserstein distances, or Optimal Transport methods more generally, offer a
powerful non-parametric toolbox to conceptualise and quantify model
uncertainty in diverse applications. Importantly, they work across the
spectrum: from small uncertainty …
An important problem in machine learning and computational statistics is to
sample from an intractable target distribution, e.g. to sample or compute
functionals (expectations, normalizing constants) of the target distribution.
This sampling problem …
This talk focuses on the central role played by optimal transport theory in the study of incomplete econometric models. Incomplete econometric models are designed to analyze microeconomic data within the constraints of microeconomic theoretic …
This talk will present the framework of weak optimal transport which allows to incorporate more general penalizations on elementary mass transports. After recalling general duality results and different optimality criteria, we will focus on recent …
Stein's method is a set of techniques for bounding distances between probability measures via integration-by-parts formulas. It was introduced by Stein in the 1980s for bouding the rate of convergence in central limit theorems, and has found many …
Generative models such as Generative Adversarial Nets (GANs), Variational Autoencoders and Normalizing Flows have been very successful in the unsupervised learning task of generating samples from a high-dimensional probability distribution. However, …
The theory of optimal transport (OT) gives rise to distance measures between probability distributions that take the geometry of the underlying space into account. OT is often used in the analysis of point cloud data, for example in domain adaptation …
The Banff International Research Station will host the "Entropic
Regularization of Optimal Transport and Applications" workshop in Banff from
June 20 to June 25, 2021.