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)
Invited Participants

Pablo Barcelo (Pontificia Universidad Catolica de Chile), Michael Benedikt (University of Oxford), Nadav Dym (Technion), Floris Geerts (University of Antwerp), Christoph Hertrich (University of Technology Nuremberg), Stefanie Jegelka (TU Munich), Imre Kondor (University of Chicago), Daniel Kral (Leipzig University), Hannah Lawrence (MIT), Ron Levie (Technion), Andrea Lodi (Cornell Tech), Yuxin Ma (Johns Hopkins University), Haggai Maron (Technion Israel Institute of Technology and NVidia), Anthea Monod (Imperial College London), Christopher Morris (RWTH Aachen University), Mathias Niepert (University of Stuttgart), Axel Parmentier (Ecole Nationale des Ponts et Chaussées), Mircea Petrache (PUC Chile), Bruno Riberio (Purdue University), Luana Rubini Ruiz (Johns Hopkins University), Amin Saberi (Stanford University), Tim Seppelt (IT University of Copenhagen), Bartolomeo Stellato (Princeton University), Samuel Vaiter (CNRS), Ameya Velingker (Independent Researcher), Soledad Villar (Johns Hopkins University), Ellen Vitercik (Stanford University), Yusu Wang (UCSD), Melanie Weber (Harvard University)

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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