795 citations · 1.4k across the 64 of their papers we have counts for
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Compressive Hermite interpolation: sparse, high-dimensional approximation from gradient-augmented measurements
Ben Adcock, Yi Sui
We consider the sparse polynomial approximation of a multivariate function on a tensor product domain from samples of both the function and its gradient. When only function samples…
Correcting for unknown errors in sparse high-dimensional function approximation
Ben Adcock, Anyi Bao, Simone Brugiapaglia
We consider sparsity-based techniques for the approximation of high-dimensional functions from random pointwise evaluations. To date, almost all the works published in this field c…
Robustness to unknown error in sparse regularization
Simone Brugiapaglia, Ben Adcock
Quadratically-constrained basis pursuit has become a popular device in sparse regularization; in particular, in the context of compressed sensing. However, the majority of theoreti…
Compressed sensing approaches for polynomial approximation of high-dimensional functions
Ben Adcock, Simone Brugiapaglia, Clayton G. Webster
In recent years, the use of sparse recovery techniques in the approximation of high-dimensional functions has garnered increasing interest. In this work we present a survey of rece…
Compressed sensing with sparse corruptions: Fault-tolerant sparse collocation approximations
Ben Adcock, Anyi Bao, John D. Jakeman +1
The recovery of approximately sparse or compressible coefficients in a Polynomial Chaos Expansion is a common goal in modern parametric uncertainty quantification (UQ). However, re…
Recovery guarantees for compressed sensing with unknown errors
Simone Brugiapaglia, Ben Adcock, Richard K. Archibald
From a numerical analysis perspective, assessing the robustness of l1-minimization is a fundamental issue in compressed sensing and sparse regularization. Yet, the recovery guarant…