Christian Rieger

According to our database1, Christian Rieger authored at least 18 papers between 2005 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

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Bibliography

2023
Spectral stability and perturbation results for kernel differentiation matrices on the sphere.
CoRR, 2023

Kernel Multi-Grid on Manifolds.
CoRR, 2023

Highly Localized RBF Lagrange Functions for Finite Difference Methods on Spheres.
CoRR, 2023

Extending error bounds for radial basis function interpolation to measuring the error in higher order Sobolev norms.
CoRR, 2023

2021
Discrete Projections: A Step Towards Particle Methods on Bounded Domains without Remeshing.
SIAM J. Sci. Comput., 2021

On the Numerical Approximation of the Karhunen-Loève Expansion for Random Fields with Random Discrete Data.
CoRR, 2021

2019
A Representer Theorem for Deep Kernel Learning.
J. Mach. Learn. Res., 2019

A Particle Method without Remeshing.
CoRR, 2019

2018
An inverse theorem for compact Lipschitz regions in ℝ<sup>d</sup> using localized kernel bases.
Math. Comput., 2018

ε-dimension in infinite dimensional hyperbolic cross approximation and application to parametric elliptic PDEs.
J. Complex., 2018

Regularized Kernel-Based Reconstruction in Generalized Besov Spaces.
Found. Comput. Math., 2018

2017
Sampling inequalities for sparse grids.
Numerische Mathematik, 2017

Reproducing Kernel Hilbert Spaces for Parametric Partial Differential Equations.
SIAM/ASA J. Uncertain. Quantification, 2017

2015
Multiscale Approximation and Reproducing Kernel Hilbert Space Methods.
SIAM J. Numer. Anal., 2015

2010
Sampling inequalities for infinitely smooth functions, with applications to interpolation and machine learning.
Adv. Comput. Math., 2010

2009
Deterministic Error Analysis of Support Vector Regression and Related Regularized Kernel Methods.
J. Mach. Learn. Res., 2009

2008
Sampling and Stability.
Proceedings of the Mathematical Methods for Curves and Surfaces, 2008

2005
Approximate Interpolation with Applications to Selecting Smoothing Parameters.
Numerische Mathematik, 2005


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