Schedule

Tuesday, May 23, 2023

10:30 - 11:00 Welcome + Coffee
11:00 - 11:45 Reza Gheissari “High-Dimensional Limit Theorems for Stochastic Gradient Descent”
11:45 - 1:00 Poster Session + Lunch
1:00 - 1:30 Nikhil Ghosh “A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors”
1:35 - 2:05 Jingfeng Wu “Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability”
2:05 - 3:05 Research Speed Dating
3:05 - 3:35 Break
3:35 - 4:05 Lijia Zhou “Agnostic Interpolation Learning Beyond Linear Regression”
4:05 - 4:35 Kangjie Zhou “Learning Time-Scales in Two-Layers Neural Networks”
4:35 - 6:00 Breakout / Small Groups / Open Problems
6:00 - 8:00 Cocktail Reception at TTIC

9:45 - 10:00 Breakfast
10:00 - 10:30 Promit Ghosal “Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent”
10:30 - 10:40 Break
10:40 - 11:30 Francesca Mignacco “Statistical Physics Insights into the Dynamics of Learning Algorithms”
11:30 - 1:00 Lunch + Break
1:00 - 2:00 Panel Discussion
2:00 - 2:30 Parthe Pandit “Mechanism of Feature Learning in Deep Fully Connected Networks and Kernel Machines that Recursively Learn Features”
2:30 - 2:45 Break
2:45 - 3:30 Matus Telgarsky “Representational strengths and limitations of transformers”
3:30 - 7:30 Picnic at The Point (Google Maps)