194 citations · 1.5k across the 76 of their papers we have counts for
85 papers
Weighted variation spaces and approximation by shallow ReLU networks
Ronald DeVore, Robert D. Nowak, Rahul Parhi +1
We investigate the approximation of functions on a bounded domain by the outputs of single-hidden-layer ReLU neural networks of width . This form of…
Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression
Joseph Shenouda, Rahul Parhi, Kangwook Lee +1
This paper introduces a novel theoretical framework for the analysis of vector-valued neural networks through the development of vector-valued variation spaces, a new class of repr…
Filtered Iterative Denoising for Linear Inverse Problems
Danica Fliss, Willem Marais, Robert D. Nowak
Iterative denoising algorithms (IDAs) have been tremendously successful in a range of linear inverse problems arising in signal and image processing. The classic instance of this i…
A Fully First-Order Method for Stochastic Bilevel Optimization
Jeongyeol Kwon, Dohyun Kwon, Stephen Wright +1
We consider stochastic unconstrained bilevel optimization problems when only the first-order gradient oracles are available. While numerous optimization methods have been proposed…
Deep Learning Meets Sparse Regularization: A Signal Processing Perspective
Rahul Parhi, Robert D. Nowak
Deep learning has been wildly successful in practice and most state-of-the-art machine learning methods are based on neural networks. Lacking, however, is a rigorous mathematical t…
Active Learning with Neural Networks: Insights from Nonparametric Statistics
Yinglun Zhu, Robert Nowak
Deep neural networks have great representation power, but typically require large numbers of training examples. This motivates deep active learning methods that can significantly r…