About

Motivated by the proliferation of machine learning methods in increasingly diverse settings, this workshop aims to bring together researchers and thinkers to reflect upon generalization within all society-facing disciplines and applications of machine learning. We will characterize not just what it means for a machine learning model to do well on future data, but more generally for any entity to behave effectively in unknown future settings. The schedule will complement traditional talks with fireside chats and debates that focus on the future of mathematics and theory in machine learning, and even the general future roles of ML in society.

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

Chairs/Organizers
Nati Srebro
Nati Srebro (Toyota Technological Institute at Chicago)
Invited Participants

Jimmy Ba (xAI), Seanna Coulson (UC San Diego), David Donoho (Stanford University), Alison Gopnik (UC Berkeley), Pavel Izmailov (Anthropic), Florent Krzakala (École polytechnique fédérale de Lausanne), Susan Murphy (Harvard University), Benjamin Recht (UC Berkeley), Robert Schapire (Microsoft Research), Adam Tauman Kalai (Open AI), Rebecca Willett (University of Chicago), Chiyuan Zhang (Google Research)

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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