112 citations · 189 across the 3 of their papers we have counts for
5 papers
Learning One-hidden-layer Neural Networks with Landscape Design
Rong Ge, Jason D. Lee, Tengyu Ma
We consider the problem of learning a one-hidden-layer neural network: we assume the input is from Gaussian distribution and the label , w…
First-order Methods Almost Always Avoid Saddle Points
Jason D. Lee, Ioannis Panageas, Georgios Piliouras +3
We establish that first-order methods avoid saddle points for almost all initializations. Our results apply to a wide variety of first-order methods, including gradient descent, bl…
Gradient Descent Can Take Exponential Time to Escape Saddle Points
Simon S. Du, Chi Jin, Jason D. Lee +3
Although gradient descent (GD) almost always escapes saddle points asymptotically [Lee et al., 2016], this paper shows that even with fairly natural random initialization schemes a…
Gradient Descent Converges to Minimizers
Jason D. Lee, Max Simchowitz, Michael I. Jordan +1
We show that gradient descent converges to a local minimizer, almost surely with random initialization. This is proved by applying the Stable Manifold Theorem from dynamical system…
Selective Inference and Learning Mixed Graphical Models
Jason D. Lee
This thesis studies two problems in modern statistics. First, we study selective inference, or inference for hypothesis that are chosen after looking at the data. The motiving appl…