60 citations · 202 across the 22 of their papers we have counts for
6 papers · 1 filter
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…
Optimal Approximation of Zonoids and Uniform Approximation by Shallow Neural Networks
Jonathan W. Siegel
We study the following two related problems. The first is to determine to what error an arbitrary zonoid in can be approximated in the Hausdorff distance by a su…
Sharp Convergence Rates for Matching Pursuit
Jason M. Klusowski, Jonathan W. Siegel
We study the fundamental limits of matching pursuit, or the pure greedy algorithm, for approximating a target function by a linear combination of elements from a di…
Entropy-based convergence rates of greedy algorithms
Yuwen Li, Jonathan Siegel
We present convergence estimates of two types of greedy algorithms in terms of the metric entropy of underlying compact sets. In the first part, we measure the error of a standard…
Nesterov acceleration despite very noisy gradients
Kanan Gupta, Jonathan W. Siegel, Stephan Wojtowytsch
We present a generalization of Nesterov's accelerated gradient descent algorithm. Our algorithm (AGNES) provably achieves acceleration for smooth convex and strongly convex minimiz…
Sharp Lower Bounds on Interpolation by Deep ReLU Neural Networks at Irregularly Spaced Data
Jonathan W. Siegel
We study the interpolation power of deep ReLU neural networks. Specifically, we consider the question of how efficiently, in terms of the number of parameters, deep ReLU networks c…