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Optimal Transport
Cargese Workshop

April 8–12th, 2024

Download the program here!

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With this event we will bring together a mix of researchers from different fields,  such as analysis, probability, applied mathematics, economics, machine learning, statistics, and optimization. The goal is to share important advances in optimal transport and its applications, but also to collectively and critically assess recent developments, so as to shape a vision for the future of the field. Through presentations and interactive activities, participants will be invited to criticize research directions and to argue in favor of others. Participants are selected by a scientific committee composed of a diverse combination of established and emerging leaders in the field.

Monday 8: Mathematical OT

Day 1 will start in the early afternoon with an overview lecture on the state of the field of optimal transport (OT). The lecture will seek to understand the success of OT in theory and applications “beyond the hype”.

 

It will then follow with a session presenting entropic OT and new theoretical results for this long-lasting problem.

Wednesday 10: OT Econ & Finance

The morning of Day 3 will cover OT economics and finance.

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The afternoon will be dedicated to scientific interactions among participants.

Friday 12: Variants of OT, Regularized.

Finally, the morning of Day 5 will cover variants of OT such as regularized, semi-discrete, causal and weak OT.

 

In the afternoon, a panel discussion will explore emerging challenges and new horizons of OT. 

Tuesday 9: Machine Learning & Statistics

Day 2 in the morning will be dedicated to OT methods in machine learning and neural networks.

 

The afternoon will be dedicated to applications to data science and statistics.

 

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Thursday 11: PDEs, Data Science, Economics.

Day 4 will open up in the morning to a dynamic picture by exploring the link between OT, partial differential equations (PDEs), diffusions, and data science.

 

In the afternoon, the focus will move to economic applications. 

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