77 citations · 318 across the 57 of their papers we have counts for
5 papers · 1 filter
Average Gradient Outer Product in kernel regression provably recovers the central subspace for multi-index models
Libin Zhu, Damek Davis, Dmitriy Drusvyatskiy +1
We study a prototypical situation when a learned predictor can discover useful low-dimensional structure in data, while using fewer samples than are needed for accurate prediction.…
Iteratively reweighted kernel machines efficiently learn sparse functions
Libin Zhu, Damek Davis, Dmitriy Drusvyatskiy +1
The impressive practical performance of neural networks is often attributed to their ability to learn low-dimensional data representations and hierarchical structure directly from…
Online Covariance Estimation in Nonsmooth Stochastic Approximation
Liwei Jiang, Abhishek Roy, Krishna Balasubramanian +3
We consider applying stochastic approximation (SA) methods to solve nonsmooth variational inclusion problems. Existing studies have shown that the averaged iterates of SA methods e…
Linear Recursive Feature Machines provably recover low-rank matrices
Adityanarayanan Radhakrishnan, Mikhail Belkin, Dmitriy Drusvyatskiy
A fundamental problem in machine learning is to understand how neural networks make accurate predictions, while seemingly bypassing the curse of dimensionality. A possible explanat…
Catalyst Acceleration for Gradient-Based Non-Convex Optimization
Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy +2
We introduce a generic scheme to solve nonconvex optimization problems using gradient-based algorithms originally designed for minimizing convex functions. Even though these method…