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Optimal Transport and Machine Learning
Olivier Bousquet · Marco Cuturi · Gabriel Peyré · Fei Sha · Justin Solomon

Sat Dec 08:00 AM -- 06:30 PM PST @ Hyatt Hotel, Seaview Ballroom
Event URL: http://otml17.marcocuturi.net »

Optimal transport (OT) is gradually establishing itself as a powerful and essential tool to compare probability measures, which in machine learning take the form of point clouds, histograms, bags-of-features, or more generally datasets to be compared with probability densities and generative models. OT can be traced back to early work by Monge, and later to Kantorovich and Dantzig during the birth of linear programming. The mathematical theory of OT has produced several important developments since the 90's, crowned by Cédric Villani's Fields Medal in 2010. OT is now transitioning into more applied spheres, including recent applications to machine learning, because it can tackle challenging learning scenarios including dimensionality reduction, structured prediction problems that involve histograms, and estimation of generative models in highly degenerate, high-dimensional problems. This workshop will follow that organized 3 years ago (NIPS 2014) and will seek to amplify that trend. We will provide the audience with an update on all of the very recent successes brought forward by efficient solvers and innovative applications through a long list of invited talks. We will add to that a few contributed presentations (oral, and, if needed posters) and, finally, a panel for all invited speakers to take questions from the audience and formulate more nuanced opinions on this nascent field.

08:00 AM Structured Optimal Transport (with T. Jaakkola, S. Jegelka) (Contributed 1)|| David Alvarez Melis
08:20 AM Approximate Bayesian computation with the Wasserstein distance (Invited 1)|| Pierre E Jacob
09:00 AM Gradient flow in the Wasserstein metric (Invited 2)|| Katy Craig
09:40 AM Approximate inference with Wasserstein gradient flows (with T. Poggio) (Contributed 2)|| Charlie Frogner
10:00 AM 6 x 3 minutes spotlights (Poster Spotlights)|| Rémi Flamary, Yongxin Chen, Napat Rujeerapaiboon, Jonas Adler, John Lee, Lucas R Roberts
11:00 AM Optimal planar transport in near-linear time (Invited 3)|| Alexandr Andoni
11:40 AM Laplacian operator and Brownian motions on the Wasserstein space (Invited 4)|| Wilfrid Gangbo
01:40 PM Geometrical Insights for Unsupervised Learning (Invited 6)|| Leon Bottou
02:20 PM Improving GANs Using Optimal Transport (with H. Zhang, A. Radford, D. Metaxas) (Contributed 3)|| Tim Salimans
02:40 PM Overrelaxed Sinkhorn-Knopp Algorithm for Regularized Optimal Transport (with L. Chizat, C. Dossal, N. Papadakis) (Contributed 4)|| Alexis THIBAULT
03:30 PM Domain adaptation with optimal transport : from mapping to learning with joint distribution (Invited 6)|| Rémi Flamary
04:10 PM Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance (Invited 7)|| Francis Bach
04:50 PM 7 x 3 minutes spotlights (Poster Spotlights)|| Elsa Cazelles, Aude Genevay, Gonzalo Mena, Christoph Brauer, Asja Fischer, Henning Petzka, Vivien Seguy, Antoine Rolet, Sho Sonoda
05:10 PM short Q&A session with plenary speakers (Roundtable)||
05:30 PM Closing session (Poster Session)||

Author Information

Olivier Bousquet (Google Brain (Zurich))
Marco Cuturi (Google Brain & CREST - ENSAE)

Marco Cuturi is a research scientist at Google AI, Brain team in Paris. He received his Ph.D. in 11/2005 from the Ecole des Mines de Paris in applied mathematics. Before that he graduated from National School of Statistics (ENSAE) with a master degree (MVA) from ENS Cachan. He worked as a post-doctoral researcher at the Institute of Statistical Mathematics, Tokyo, between 11/2005 and 3/2007 and then in the financial industry between 4/2007 and 9/2008. After working at the ORFE department of Princeton University as a lecturer between 2/2009 and 8/2010, he was at the Graduate School of Informatics of Kyoto University between 9/2010 and 9/2016 as a tenured associate professor. He joined ENSAE in 9/2016 as a professor, where he is now working part-time. His main employment is now with Google AI (Brain team in Paris) since 10/2018, as a research scientist working on fundamental aspects of machine learning.

Gabriel Peyré (Université Paris Dauphine)
Fei Sha (University of Southern California (USC))
Justin Solomon (Stanford University)

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