Keynote Talk : Optimal transport for machine learning


Optimal transport (OT) (a.k.a Wasserstein distance) has been the subject of significant interest in the past decade from themachine learning community.  Due to its geometrical properties, OT is naturally the go-to tool in several applications in transfer learning, domain adaptation, computer graphics, medical imaging and generative diffusion models. It has been the focus of different Communities: in economics (Kantorovich, Nobel prize 1975), mathematics and statistics (C. Villani and Figalli Fields medal 2010, 2018), physics (Schrodinger 1932) and now machine learning. In this tutorial, I will give an overview of Optimal transport, from the historical Monge problem to its trending entropy regularization. The focus will be on practical aspects of OT and its applications in machine learning and signal processing. 


February 29 ,2024 March 11 ,2024

Abstract submission deadline

March 7 ,2024 March 18 ,2024

Paper submission deadline

April 22 ,2024

Accept/Reject notification

May 12 ,2024

Camera ready copy due

May 27-28 ,2024

Metis Spring school

May 29-31 ,2024

Netys Conference


Revised selected papers will be published as a post-proceedings in Springer's LNCS "Lecture Notes in Computer Science"

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