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stat.ML2026
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.…
stat.ML2025
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…
stat.ML2025
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…