43 citations · 280 across the 42 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…
Clustering a Mixture of Gaussians with Unknown Covariance
Damek Davis, Mateo Díaz, Kaizheng Wang
We investigate a clustering problem with data from a mixture of Gaussians that share a common but unknown, and potentially ill-conditioned, covariance matrix. We start by consideri…
A SMART Stochastic Algorithm for Nonconvex Optimization with Applications to Robust Machine Learning
Aleksandr Aravkin, Damek Davis
In this paper, we show how to transform any optimization problem that arises from fitting a machine learning model into one that (1) detects and removes contaminated data from the…