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Sat Dec 13 05:30 AM -- 03:30 PM (PST) @ Level 5; room 512 c, g
Optimal Transport and Machine Learning
Marco Cuturi · Gabriel Peyré · Justin Solomon · Alexander Barvinok · Piotr Indyk · Robert McCann · Adam Oberman

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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.