The Machine Learning Lunch is a weekly 20-minute talk series for folks in the Vector Institute and University of Toronto ecosystem who are interested in machine learning. The talks are intended for a broad ML audience, with the goal of appealing to anyone who is interested in how to learn from data. Presenters are graduate students from multiple groups and research areas, covering the spectrum between theory and applications. Lunch is provided.
When: Fridays 12pm - 1pm (with a few exceptions, see detailed schedule below)
Where: Schwartz Reisman Institute, 11th Floor, Sobolev Space
Who: You, if you are affiliated with the Vector Institute or the University of Toronto and interested in machine learning!
Food: Free, provided by our sponsors.
Slack: Join #ml-lunch-vector on the Vector Institute slack for updates!
Mailing list: Join our google group to receive updates via email
Contact: ml-lunch-committee@cs.toronto.edu
Attendance Form (optional): let us know when you're coming!
Upcoming Speakers
The organizing committee is still preparing the initial speaker roster. Check back for more details soon!
| Date | Presenter | Title |
|---|---|---|
| Oct. 10, 2024 (12:10-12:40pm, Vector Research Day) | Ayoub El Hanchi | Does Deep Learning Require Rethinking Generalization? |
| Oct. 18, 2024 | Jonas Guan | Deep Learning Has Issues, Can Biology Help Fix Them? |
| Oct. 25, 2024 | Andreas Burger | Smaller, Faster, Better Forces on Molecules with Deep Equilibrium Models |
| Nov. 1, 2024 | Alireza Mousavi | Towards a Theory of Feature Learning Under Adversarial Robustness |
| Nov. 15, 2024 | Nikita Dhawan | Scaling Causal Inference for Real-World Healthcare Challenges |
| Nov. 22, 2024 | Michael Cooper | (TBD) |
| Nov. 29, 2024 | Sierra Wyllie | Models Cause Feedback Loops in Data, What's Happening? |
| Dec. 6, 2024 | (TBD) | (TBD) |
| ... | ... | ... |
Format and Theme
For the Fall 2024 semester, our talk theme is: Discoveries and Mysteries.
-
Student talk (~20 minutes):
- Discoveries (~10 minutes): One or two recent results that set the stage for the second half. The results may be derived from your own research or previous work. Consider taking a critical eye towards the presented results and discussing their limitations as well.
- Mysteries (~10 minutes): One or two open questions, or areas you think are currently overlooked or misunderstood. Each mystery should be motivated, e.g. by the discoveries in the first half or by preliminary results of your own work. Consider explaining what is counterintuitive about each mystery or giving a hypothesis for what a solution could look like.
- Audience questions (~10 minutes)
- Socializing, unstructured discussion, and finishing lunch (30 minutes)
Possible themes for future semesters:
- Things I Wish I Knew About ___ (Tutorial)
- How Did We Get Here? (Historical review)
- I Can't Believe It's Not Better
- Folk Knowledge
Please let us know if you have ideas for themes!
How do I get involved?
You can find out about new talks as they happen by joining the #ml-lunch-vector channel on the Vector Institute slack.
If you are a grad student and would like to present, please email us at ml-lunch-committee@cs.toronto.edu, or ping one of the organizing committee members on the Vector Institute slack. We will follow up with you to schedule your talk!
We highly encourage all grad student attendees of the ML Lunch talks to sign up as presenters, to ensure that the talks represent the full machine learning community at the Vector Institute and the University of Toronto. Don't worry if you don't have a polished presentation yet, we are happy to accept talks on in-progress work and open questions, and can work with you to ensure your talk appeals to a broad audience!
Organizing Committee
- Anvith Thudi (Executive Chair)
- Viet Nguyen (Master of Ceremonies)
- Ella Rajaonson (Communications Chair)
- Mert Vural (Food Chair)
- Daniel Johnson (CTO)
- Chris J. Maddison
Faculty Sponsors
- Rahul Krishnan
- Igor Gilitschenski
- Chris J. Maddison
- Sheila McIlraith
- Colin Raffel
Institute Sponsors
- Vector Institute
- Acceleration Consortium