Paper Who presents Date
Spring 2018    
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization Zhan Lin Mar 30
An Alternative View: When Does SGD Escape Local Minima? Sen Na Mar 15
Stronger generalization bounds for deep nets via a compression approach Xialiang Dou Apr 6
Risk and parameter convergence of logistic regression Sen Na Apr 13, 20
Gradient Descent Quantizes ReLU Network Features Zhan Lin May 25
Generalization in Machine Learning via Analytical Learning Theory    
Sever: A Robust Meta-Algorithm for Stochastic Optimization    
Representing smooth functions as compositions of near-identity functions with implications for deep network optimization    
Are ResNets Provably Better than Linear Predictors? Xialiang Dou Apr 27
Representing smooth functions as compositions of near-identity functions with implications for deep network optimization Zhan Lin May 4
On the Convergence of Adam and Beyond Xialiang Dou May 11
Fall 2018    
How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift)    
Efficient Statistics, in High Dimensions, from Truncated Samples Sen Na Oct 24
Central Limit Theorems and Bootstrap in High Dimensions Zhan Lin Nov 1
Breaking the Curse of Dimensionality with Convex Neural Networks Xialiang Dou Oct 10
Estimating Information Flow in Neural Networks    
A Mean Field View of the Landscape of Two-Layers Neural Networks Xialiang Dou Nov 8
On exponential convergence of SGD in non-convex over-parametrized learning Sen Na Nov 15
A Convergence Theory for Deep Learning via Over-Parametrization Zhan Lin Nov 29