Optimal Transport meets Probability, Statistics and Machine Learning
Videos from CMO Workshop
Dejan Slepcev, Carnegie Mellon University
Monday May 1, 2017 09:00 - 09:55
Consistency of objective functionals in semi-supervised learning
Youssef Marzouk, Massachusetts Institute of Technology
Monday May 1, 2017 11:00 - 11:55
Inference via low-dimensional couplings
Esteban Tabak, Courant Institute
Monday May 1, 2017 12:00 - 12:53
Explanation of variability through optimal transport
Elsa Cazelles, Institut de Mathématiques de Bordeaux
Monday May 1, 2017 13:00 - 13:20
Regularization of Barycenters in the Wasserstein Space
Jan Obloj, University of Oxford
Monday May 1, 2017 15:00 - 15:50
Martingale Optimal Transport: at the crossroad of mathematical finance, probability and optimal transport
Gaoyue Guo, Oxford University
Monday May 1, 2017 16:30 - 16:57
Numerical computation of martingale optimal transport on real line
Young-Heon Kim, University of British Columbia
Monday May 1, 2017 17:03 - 17:32
Optimal martingale transport
Bruno Lévy, Inria
Tuesday May 2, 2017 09:02 - 09:59
Some algorithmic aspects of semi-discrete optimal transport.
Tryphon Georgiou, University of California, Irvine
Tuesday May 2, 2017 10:01 - 10:30
Optimal mass transport and density flows
Bertram Düring, University of Warwick
Tuesday May 2, 2017 11:00 - 11:51
Lagrangian schemes for Wasserstein gradient flows
Clarice Poon, University of Cambridge
Tuesday May 2, 2017 12:34 - 13:00
The total variation Wasserstein gradient flow
Jean-Michel Loubes, Université de Toulouse
Tuesday May 2, 2017 16:32 - 17:04
Transport based kernels for Gaussian Process Modeling
Sebastian Reich, Universität Potsdam
Wednesday May 3, 2017 09:03 - 09:34
Optimal transport and its use in data assimilation and sequential Bayesian inference
Espen Bernton, Columbia University
Wednesday May 3, 2017 09:37 - 10:01
Inference in generative models using the Wasserstein distance
Christian Léonard, Universite Paris Nanterre
Wednesday May 3, 2017 11:01 - 12:01
Some results about entropic transport
Adam Oberman, McGill
Thursday May 4, 2017 09:03 - 10:01
PDE approach to regularization in deep learning
Rémi Flamary, Université de Nice Sophia Antipolis
Thursday May 4, 2017 10:03 - 10:33
Joint distribution optimal transportation for domain adaptation
Jianbo Ye, Penn State University
Thursday May 4, 2017 11:04 - 11:32
New numerical tools for optimal transport and their machine learning applications
Jun Kitagawa, Michigan State University
Thursday May 4, 2017 11:37 - 12:17
On the multi-marginal optimal partial transport and partial barycenter problems
Christoph Brune, University of Twente
Thursday May 4, 2017 12:30 - 13:00
Combined modelling of optimal transport and segmentation
