We introduce a general framework for active learning in regression problems. Our
framework extends the standard setup by allowing for general types of data, rather than …

Least-squares approximation is one of the most important methods for recovering an
unknown function from data. While in many applications the data is fixed, in many others …

Many problems arising in computational science and engineering can be described in terms
of approximating a smooth function of variables, defined over an unknown domain of …

The probability and evidence theories are frequently used tool to deal with the mixture of
aleatory and epistemic uncertainties. Due to the double-loop procedure for the mixed …

We study generative compressed sensing when the measurement matrix is randomly
subsampled from a unitary matrix (with the DFT as an important special case). It was recently …

Many problems in computational science and engineering can be cast as approximating a
high-dimensional function from data. These types of problems involves at least three main …

Many problems in computational science, engineering, and uncertainty quantification (UQ)
require the approximation of Partial Differential Equations (PDEs) from corrupted data, which …

Active learning is an important concept in machine learning, in which the learning algorithm
is able to choose where to query the underlying ground truth to improve the accuracy of the …