Optimal Transport and Machine Learning

Workshop at Neural Information Processing Systems (NeurIPS) 2023

Over the last decade, optimal transport (OT) has evolved from a prize-winning research area in pure mathematics to a recurring theme bursting across many areas of machine learning (ML). Advancements in OT theory, computation, and statistics have fueled breakthroughs in a wide range of applications, from single-cell genomics to generative modeling and the optimization of over-parametrized neural nets , among many others. The OTML workshop series (in '14,~'17,~'19, and '21) has been instrumental in shaping this influential research thread. The OTML workshop aims to provide a unique platform to federate, disseminate, and advance current knowledge in this rapidly growing field.

Call for papers

We invite researcher in optimal transport and machine learning to submit their latest works to our workshop.
Extended deadline for submissions is October 3rd, 2023 AoE
Topics include but are not limited to (see Call for Papers for more details):

  • Optimal Transport Theory
  • Generalizations of Optimal Transport
  • Computational and Statistical Optimal Transport
  • Optimal Transport for Machine Learning and Applications

Confirmed Speakers

  • Felix Otto (Max Planck Institute)
  • Laetitia Chapel (Institute Agro Rennes-Angers)
  • Rémi Flamary (École Polytechnique)
  • Brandon Amos (Meta AI)
  • Florentina Bunea (Cornell University)
  • Smita Krishnaswamy (Yale University)
  • Sinho Chewi (Institute for Advanced Study)


David Alvarez-Melis

Microsoft Research & Harvard University

Charlotte Bunne

ETH Zurich

Marco Cuturi


Ziv Goldfeld

Cornell University

Anna Korba


Aram-Alexandre Pooladian

New York University


Contact us at otml2023workshop@gmail.com.