68 citations · 133 across the 8 of their papers we have counts for
3 papers · 1 filter
Universality of high-dimensional scaling limits of stochastic gradient descent
Reza Gheissari, Aukosh Jagannath
We consider statistical tasks in high dimensions whose loss depends on the data only through its projection into a fixed-dimensional subspace spanned by the parameter vectors and c…
High-dimensional limit theorems for SGD: Effective dynamics and critical scaling
Gerard Ben Arous, Reza Gheissari, Aukosh Jagannath
We study the scaling limits of stochastic gradient descent (SGD) with constant step-size in the high-dimensional regime. We prove limit theorems for the trajectories of summary sta…
Online stochastic gradient descent on non-convex losses from high-dimensional inference
Gerard Ben Arous, Reza Gheissari, Aukosh Jagannath
Stochastic gradient descent (SGD) is a popular algorithm for optimization problems arising in high-dimensional inference tasks. Here one produces an estimator of an unknown paramet…