Convergence Rates of Spectral Regularization Methods: A Comparison between Ill-Posed Inverse Problems and Statistical Kernel Learning

Asymptotic properties of nonparametric inference (62G20) Computational learning theory (68Q32) Numerical solutions of ill-posed problems in abstract spaces; regularization (65J20)

Related Items (2)

Moving least squares approximation using variably scaled discontinuous weight function ⋮ Learning with partition of unity-based kriging estimators

Uses Software

ElemStatLearn

Cites Work

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Regularization theory for ill-posed problems. Selected topics
Optimal learning rates for least squares regularized regression with unbounded sampling
Optimal rates for regularization of statistical inverse learning problems
Regularization in kernel learning
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Optimal Convergence Rates Results for Linear Inverse Problems in Hilbert Spaces
DISCRETIZATION ERROR ANALYSIS FOR TIKHONOV REGULARIZATION
Regularization of some linear ill-posed problems with discretized random noisy data
Learning Theory
Support Vector Machines
Analysis of Profile Functions for General Linear Regularization Methods
Spectral Algorithms for Supervised Learning
On Convergence of Kernel Learning Estimators
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Discretization strategy for linear ill-posed problems in variable Hilbert scales
Solving ill-posed inverse problems using iterative deep neural networks
Statistical Inverse Estimation in Hilbert Scales
Computational Methods for Inverse Problems
Convergence analysis of (statistical) inverse problems under conditional stability estimates
Convergence Rates of General Regularization Methods for Statistical Inverse Problems and Applications
How general are general source conditions?
Convergence rates for Tikhonov regularization from different kinds of smoothness conditions
Theory of Reproducing Kernels
Linear integral equations
The elements of statistical learning. Data mining, inference, and prediction

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