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Optimal Transport and Machine Learning
Marco Cuturi · Gabriel Peyré · Justin Solomon · Alexander Barvinok · Piotr Indyk · Robert McCann · Adam Oberman

Sat Dec 13 05:30 AM -- 03:30 PM (PST) @ Level 5; room 512 c, g
Event URL: http://www.iip.ist.i.kyoto-u.ac.jp/OTML2014/ »

Optimal transport (OT) has emerged as a novel tool to solve problems in machine learning and related fields, e.g. graphics, statistics, data analysis, computer vision, economics and imaging.

In particular, the toolbox of OT (including for instance the Wasserstein/Earth Mover's Distances) offers robust mathematical techniques to study probability measures and compare complex objects described using bags-of-features representations.

Scaling OT algorithms to datasets of large dimension and sample size presents, however, a considerable computational challenge. Taking for granted that these challenges are partially solved, there remains many salient open research questions on how to integrate OT in statistical methodologies (dimensionality reduction, inference, modeling) beyond its classical use in retrieval. OTML 2014 will be the first international workshop to address state-of-the-art research in this exciting area.

Author Information

Marco Cuturi (Université Paris-Saclay, CREST - ENSAE)

Marco Cuturi is a research scientist at Apple, 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. He was at Google between 10/2018 and 1/2022. His main employment is now with Apple, since 1/2022, as a research scientist working on fundamental aspects of machine learning.

Gabriel Peyré (Université Paris Dauphine)
Justin Solomon (Stanford University)
Alexander Barvinok (University of Michigan)
Piotr Indyk (MIT)
Robert McCann (University of Toronto)
Adam Oberman (McGill University)

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