We introduce a general framework for active learning in regression probl...
Over the last decade, approximating functions in infinite dimensions fro...
Sharpness is an almost generic assumption in continuous optimization tha...
The past decade has seen increasing interest in applying Deep Learning (...
The computation of global radial basis function (RBF) approximations req...
The problem of approximating smooth, multivariate functions from sample
...
This paper concerns the approximation of smooth, high-dimensional functi...
Sparse polynomial approximation has become indispensable for approximati...
Solving inverse problems is a fundamental component of science, engineer...
In this chapter, we discuss recent work on learning sparse approximation...
Many problems in computational science and engineering can be described ...
Motivated by the question of optimal functional approximation via compre...
We consider approximating analytic functions on the interval [-1,1] from...
The accurate approximation of scalar-valued functions from sample points...
In this paper, we consider the use of Total Variation (TV) minimization ...
The sparsity in levels model recently inspired a new generation of effec...
Deep learning (DL) is transforming whole industries as complicated
decis...
There is overwhelming empirical evidence that Deep Learning (DL) leads t...
Due to their flexibility, frames of Hilbert spaces are attractive
altern...
In this paper, we address the problem of approximating a multivariate
fu...
A signature result in compressed sensing is that Gaussian random samplin...
Infinite-dimensional compressed sensing deals with the recovery of analo...
Convolutional analysis operator learning (CAOL) enables the unsupervised...
Deep learning, due to its unprecedented success in tasks such as image
c...
We show the potential of greedy recovery strategies for the sparse
appro...
We present improved sampling complexity bounds for stable and robust spa...