All talk times are in Pacific Time. 

–
Opening Remarks
–
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds, and Benign Overfitting
Frederic Koehler (Simons Institute)
–
Adaptive Wavelet Distillation from DNNs and Dictionary Learning
Bin Yu (UC Berkeley)
–
Break
–
Graphon Neural Networks and the Transferability of Graph Neural Networks
Luiz Chamon (UC Berkeley)
–
Self-Training Converts Weak Learners to Strong Learners in Mixture Models
Spencer Frei (UC Berkeley)
–
Lunch
–
Non-Parametric Convergence Rates for Plain Vanilla Stochastic Gradient Descent
Rapha, ël Berthier
–
Sharp Matrix Concentration
March Boedihardjo (University of California, Irvine)
–
Break
–
Reception
–
Learning Staircases
Emmanuel Abbe (École polytechnique fédérale de Lausanne), Enric Boix (MIT), Theodor Misiakiewicz (Stanford University)
–
Exact Asymptotics and Universality for Gradient Flows and Empirical Risk Minimizers
Andrea Montanari (Stanford University)
–
Break
–
The Spectrum of Nonlinear Random Matrices for Ultra-Wide Neural Networks
Yizhe Zhu (University of California, Irvine)
–
Universality of Neural Networks
Dan Mikulincer (MIT)
–
Lunch
–
Is Overfitting Actually Benign? On the Consistency of Interpolating Methods
Preetum Nakkiran (UCSD)
–
On the Cryptographic Hardness of Learning One-Hidden Layer Neural Networks
Ilias Zadik (MIT)
–
Break
–
Representation Costs of Linear Neural Networks: Analysis and Design
Mina Karzand (University of California, Davis)