2 citations · 2 across the 2 of their papers we have counts for
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Approximation of Functions: Optimal Sampling and Complexity
David Krieg, Mario Ullrich
We consider approximation or recovery of functions based on a finite number of function evaluations. This is a well-studied problem in optimal recovery, machine learning, and numer…
Constructive discretization and approximation in reproducing kernel Hilbert spaces
Abdellah Chkifa, Matthieu Dolbeault, David Krieg +1
We generalize the sparsification algorithm of Batson, Spielman and Srivastava, making one part of the result dimension-independent. In particular, we recover discretization inequal…
Noisy nonlinear information and entropy numbers
David Krieg, Erich Novak, Leszek Plaskota +1
It is impossible to recover a vector from with less than linear measurements, even if the measurements are chosen adaptively. Recently, it has been shown that on…
On the power of adaption and randomization
David Krieg, Erich Novak, Mario Ullrich
We present bounds on the maximal gain of adaptive and randomized algorithms over non-adaptive, deterministic ones for approximating linear operators on convex sets. If the sets are…
Sparse grids vs. random points for high-dimensional polynomial approximation
Jakob Eggl, Elias Mindlberger, Mario Ullrich
We study polynomial approximation on a -cube, where is large, and compare interpolation on sparse grids, aka Smolyak's algorithm (SA), with a simple least squares method bas…
How many continuous measurements are needed to learn a vector?
David Krieg, Erich Novak, Mario Ullrich
One can recover vectors from with arbitrary precision, using only continuous measurements that are chosen adaptively. This surprising r…