About

Graph learning is a branch of machine learning focusing on developing and studying methods to make predictions for vertices, subgraphs, or entire graphs. The field of graph learning has already revealed many interesting connections across various areas in theoretical computer science (TCS) and mathematics, including logic, descriptive complexity, learning theory, combinatorial optimization, and geometry. 

In this workshop, we bring together researchers in graph learning who can benefit from a TCS perspective and researchers in TCS who can engage with graph learning. Our objectives are to:

  • Provide a more unified perspective on graph learning within TCS.
  • Identify the major challenges arising from the current interactions between graph learning and TCS.
  • Discover areas within TCS that could benefit from richer interaction with graph learning.

 

If you require special accommodation, please contact our access coordinator at simonsevents@berkeley.edu with as much advance notice as possible.

Please note: the Simons Institute regularly captures photos and video of activity around the Institute for use in videos, publications, and promotional materials. 

Chairs/Organizers
Haggai Maron
Haggai Maron
(Technion Israel Institute of Technology and NVidia)
Register

Registration is required for in-person attendance, access to the livestream, and early access to the recording. Space may be limited, and you are advised to register early. 

For additional information please visit: https://simons.berkeley.edu/participating-workshop.

Please note: the Simons Institute regularly captures photos and video of activity around the Institute for use in videos, publications, and promotional materials. 

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