Submissions should be uploaded on the OTML14 Easychair system before October 23, 23:59PM (PDT, Pacific Time).

We solicit submission of original research at the interface between optimal transport and machine learning, studying for instance (but not limited to):

• numerical schemes to solve the OT problem (e.g. fast EMD solvers);
• generalizations of the OT problem (e.g. multi-marginal OT);
• theoretical results on approximations of OT distances through embeddings;
• numerical resolution and theoretical study of constrained and/or regularized OT problems;
• applications of OT in supervised learning settings to handle histogram data (e.g. distance or kernel based classifiers, metric learning with the OT geometry);
• applications of OT in unsupervised learning to summarize datasets;(e.g. generalization of k-means type clustering) and/or histograms (e.g. Wasserstein barycenters, Wasserstein propagation, retrieval with the EMD);
• applications of OT to model interactions in social networks (e.g. matching models);
• applications of OT to computer vision and related fields;
• combinatorial perspectives on OT and related algebraic statistical concepts (e.g. contingency tables)

Authors can submit work that overlaps with previously published or submitted work, as long as it adds a new perspective on that work.

Submissions must adhere to NIPS 2014 style format available on the NIPS submission page. Papers may be between 6 and 10 pages long, including figures and references. Supplementary material can be provided. Submissions should not be anonymized.

All papers will undergo a reviewing process. All accepted papers will have a poster presentation and selected papers will be given oral presentation slots.