1 citations · 1 across the 8 of their papers we have counts for
9 papers
Greedy multiscale kernel algorithms
Sara Avesani, Armin Iske, Michael Multerer +1
Multiscale kernel approximation and greedy kernel algorithms are two established tools for scattered data approximation. We combine them into greedy multiscale kernel algorithms, i…
Spectral equivalence of unsymmetric kernel matrices and applications
Tizian Wenzel, Armin Iske
Symmetric kernel matrices are a well-researched topic in the literature of kernel based approximation. In particular stability properties in terms of lower bounds on the smallest e…
Learning phase-space flows using time-discrete implicit Runge-Kutta PINNs
Álvaro Fernández Corral, Nicolás Mendoza, Armin Iske +2
We present a computational framework for obtaining multidimensional phase-space solutions of systems of non-linear coupled differential equations, using high-order implicit Runge-K…
Bounds on the Generalization Error in Active Learning
Vincent Menden, Yahya Saleh, Armin Iske
We establish empirical risk minimization principles for active learning by deriving a family of upper bounds on the generalization error. Aligning with empirical observations, the…
Spectral alignment of kernel matrices and applications
Tizan Wenzel, Armin Iske
Kernel matrices are a key quantity in kernel-based approximation, and important properties such as stability and algorithmic convergence can be analyzed with their help. In this wo…
On the convergence of generalized kernel-based interpolation by greedy data selection algorithms
Kristof Albrecht, Armin Iske
We analyze the convergence of generalized kernel-based interpolation methods. This is done under minimalistic assumptions on both the kernel and the target function. On these groun…
Inducing Riesz and orthonormal bases in via composition operators
Yahya Saleh, Armin Iske
Let be a composition operator mapping into for some open sets . We characterize the mappings that transform Riesz b…
Finetuning greedy kernel models by exchange algorithms
Tizian Wenzel, Armin Iske
Kernel based approximation offers versatile tools for high-dimensional approximation, which can especially be leveraged for surrogate modeling. For this purpose, both "knot inserti…
Product kernels are efficient and flexible tools for high-dimensional scattered data interpolation
Kristof Albrecht, Juliane Entzian, Armin Iske
This work concerns the construction and characterization of product kernels for multivariate approximation from a finite set of discrete samples. To this end, we consider composing…