2025-09-30 –, Louis Armand 1 - Est
Optimal Transport (OT) is a powerful mathematical framework with applications in machine learning, statistics, and data science. This talk introduces the Python Optimal Transport toolbox (POT), an open-source library designed to efficiently solve OT problems. Attendees will learn the basics of OT, explore real-world use cases, and gain hands-on experience with POT (https://pythonot.github.io/) .
Outline
- Minutes 0-5: Introduction to Optimal Transport (What and Why)
- Minutes 5-10: Applications of OT in Machine Learning and Data Science
- Minutes 10-20: Hands-on Examples with the POT Library
- Computing Wasserstein distances
- Optimal transport variants
- Real-world use cases
- Minutes 20-25: Advanced Features and Extensions in POT
- Minutes 25-30: Q&A and Discussion
POT Documentation: https://pythonot.github.io/
POT Source code : https://github.com/PythonOT/POT
Key Takeaways
- Understand the basics of Optimal Transport and its applications.
- Learn how to use the POT library for solving OT problems.
- Gain insights into practical use cases and advanced features of POT.
Target Audience
This talk is aimed at data scientists, engineers, and researchers interested in
learning about Optimal Transport and its practical implementation in Python.
Attendees should have a basic understanding of Python and numerical optimization.
Remi Flamary is Professor at École Polytechnique in the Centre de Mathématiques Appliquées (CMAP). He was previously Associate Professor at Université Cote d’Azur (UCA), 3IA Chair in Artificial Intelligence, and a member of Lagrange Laboratory, Observatoire de la Cote d’Azur. His current research interests include signal, image processing, and machine learning with a recent focus on applications of Optimal Transport theory to machine learning problems such as graph processing and domain adaptation. He is also the co-creator and maintainer of the Python Optimal Transport toolbox (POT).