117 citations · 442 across the 44 of their papers we have counts for
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Data Complexity Estimates for Operator Learning
Nikola B. Kovachki, Samuel Lanthaler, Hrushikesh Mhaskar
Operator learning has emerged as a new paradigm for the data-driven approximation of nonlinear operators. Despite its empirical success, the theoretical underpinnings governing the…
Robust and tractable multidimensional exponential analysis
H. N. Mhaskar, S. Kitimoon, Raghu G. Raj
Motivated by a number of applications in signal processing, we study the following question. Given samples of a multidimensional signal of the form $$ f(\boldsymbol\ell)=\sum_{k=1}…
Learning on manifolds without manifold learning
H. N. Mhaskar, Ryan O'Dowd
Function approximation based on data drawn randomly from an unknown distribution is an important problem in machine learning. The manifold hypothesis assumes that the data is sampl…
An Eigenfunction Approach to Conversion of the Laplace Transform of Point Masses on the Real Line to the Fourier Domain
Michael McKenna, Hrushikesh N. Mhaskar, Richard G. Spencer
Motivated by applications in magnetic resonance relaxometry, we consider the following problem: Given samples of a function , where i…