Workshop: "Recent Advances in Graph Machine Learning"

Machine Learning on graphs, and more generally non-euclidean structured data, has recently emerged as a primary citizen of the machine learning world, with many applications in community detection, recommender systems, natural language processing, molecule classification, protein interface prediction, quantum chemistry, epidemiology, combinatorial optimization, and so on. In particular, deep models such as Graph Neural Networks have become very popular tools, with their successes, limitations, and a plethora of open questions.

This day, held in English, aims to bring together researchers and students to discuss recent advances in Graph Machine Learning, both theoretical and practical. Topics include, but are not limited to:

• Graph Neural Networks
• Statistics on graphs
• Graph Signal Processing

Invited speakers:

• Pierre Borgnat (CNRS, ENS Lyon)
• Johannes Lutzeyer (CMAP, X)
• Catherine Matias (CNRS, LPSM)
• Clotilde Melot (I2M, CMI, Université Aix Marseille)

For more information and to participate.