Tengyu Ma, Stanford University Abstract: I will discuss RL algorithms for automated theorem proving with LLMs, especially in the possible future regime where we run out of high-quality training data. To keep improving the models with limited data, we draw inspiration from mathematicians, who continuously develop new results, partly by proposing novel conjectures or exercises […]

Nigel Goldenfeld, UC San Diego Department of Physics and HDSI Abstract: The remarkable ability of modern neural networks to generalize improves with increasing network capacity, even when the number of model parameters or effective degrees of freedom exceeds the number of training data points. This phenomenon is all the more surprising given that generalization error […]

Max Z. Li, University of Michigan Abstract: Ground Delay Programs (GDPs) mitigate air traffic demand-capacity imbalances by holding flights on the ground at their origin airports when the destination airport's arrival capacity is reduced, thereby limiting costly airborne holding. A central challenge is that day-to-day demand-capacity balancing depends on accurate capacity predictions. In practice, however, […]