ICLR 2024 Social on Open Collaboration in ML Research

MLC Social is back! After a few years of virtual socials held at NeurIPS 2020, ICLR 2021, ICML 2021, and a successful in-person lunch at NeurIPS 2022 and Deep Learning Indaba 2023, we are once again hosting a gathering for all researchers! Stay tuned for more details to come.

Date & Time

The social will be Wednesday, 8 May 2024, during and after the lunch break, 12:45 to 2:15pm CEST (local time in Vienna). It will be in person only.

What it is

Making AI research more inviting, inclusive, and accessible is a difficult task, but the movement to do so is close to many researchers' hearts. Progress toward democratizing AI research has been centered around making knowledge (e.g. class materials), established ideas (e.g. papers), and technologies (e.g. code) more accessible. However, open, online resources are only part of the equation. Growth as a researcher requires not only learning by consuming information individually, but hands-on practice whiteboarding, coding, plotting, debugging, and writing collaboratively, with either mentors or peers.

Of course, making "collaborators" more universally accessible is fundamentally more difficult than, say, ensuring all can access arXiv papers, because scaling people and research groups is much harder than scaling websites.

Can we nevertheless make access to collaboration itself more open? Can we flatten access to peers and mentors so the opportunities available to those at the best industrial and academic labs are more broadly available to all entrants to our burgeoning field? How can we kick-start remote, non-employment based research collaborations more effectively? This social is designed to discuss these topics and help you meet potential collaborators, find interesting ideas, and kick-start your next project.


We will have 4 sections in this Social: icebreaker, two open-mic sessions, and a group exercise. You can look at the whole structure here, including a brief opening remark.


  1. Rosanne Liu, ML Collective & Google DeepMind
  2. Rahim Entezari, Stability AI
  3. Olga Saukh, TU Graz, Institute of Technical Informatics
  4. Muhtasham Oblokulov, TU Munich
  5. Raman Dutt, University of Edinburgh