Optimal transportation, at its core, is a powerful framework for obtaining structured
yet general couplings between general probability measures based on matching underlying
characteristics. This framework lends itself naturally to applications in …
Detecting differences and building classifiers between distributions,
given only finite samples, are important tasks in a number of scientific
fields. Optimal transport (OT) has evolved as the most natural concept to
measure the distance between …
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 …
The common theme of this summer school is the mathematics of Monge-Kantorovich
optimal transport (OT). The stunning mathematical development of OT has
recently permeated into several fields of applications. Our speakers, chosen
from the fields of analysis, biology, data science, economics, and probability,
are leaders in their respective fields whose work intimately involves OT. Our
goal is to expose talented students and junior researchers to the exciting and
manifold research opportunities arising from OT and its applications, through
attending lectures and interacting with the speakers as well as their peer
participants. We strongly encourage participation by a diverse audience and
welcome attendees from traditionally underrepresented socio-economic and
cultural groups.
As the school is planned at the beginning of the summer season in Seattle, the
participants can also enjoy the natural beauty of the Pacific Northwest and
feel the energy of one of leading tech metropolises in the United States and
around the world.
The common theme of this summer school is the mathematics of Monge-Kantorovich
optimal transport (OT). The stunning mathematical development of OT has
recently permeated into several fields of applications. Our speakers, chosen
from the fields of analysis, biology, data science, economics, and probability,
are leaders in their respective fields whose work intimately involves OT. Our
goal is to expose talented students and junior researchers to the exciting and
manifold research opportunities arising from OT and its applications, through
attending lectures and interacting with the speakers as well as their peer
participants. We strongly encourage participation by a diverse audience and
welcome attendees from traditionally underrepresented socio-economic and
cultural groups.
As the school is planned at the beginning of the summer season in Seattle, the
participants can also enjoy the natural beauty of the Pacific Northwest and
feel the energy of one of leading tech metropolises in the United States and
around the world.
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 …