Least squares approximations in linear statistical inverse learning problems

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Computational learning theory (68Q32) Numerical solutions of ill-posed problems in abstract spaces; regularization (65J20)

Cites Work

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Projection methods for ill-posed problems revisited
On the stability and accuracy of least squares approximations
Optimal rates for regularization of statistical inverse learning problems
Regularization in kernel learning
On regularization algorithms in learning theory
Error controlled regularization by projection
Convergence rates for regularized solutions of integral equations from discrete noisy data
On the regularization of projection methods for solving ill-posed problems
Discretization effects in statistical inverse problems
Regularisierung schlecht gestellter Probleme durch Projektionsverfahren
Nonconvergence results for the application of least-squares estimation to ill-posed problems
Statistical approach to some ill-posed problems for linear partial differential equations
Sharp adaptation for inverse problems with random noise
Best choices for regularization parameters in learning theory: on the bias-variance problem.
Nonlinear solution of linear inverse problems by wavelet-vaguelette decomposition
Convergence analysis of Tikhonov regularization for non-linear statistical inverse problems
Balancing principle in supervised learning for a general regularization scheme
Optimal rates for the regularized least-squares algorithm
Shannon sampling. II: Connections to learning theory
Learning theory estimates via integral operators and their approximations
On early stopping in gradient descent learning
Optimal discretization of inverse problems in Hilbert scales. Regularization and self-regulari\-za\-tion of projection methods
On the regularizing properties of a full multigrid method for ill-posed problems
A convergence analysis of regularization by discretization in preimage space
Two-parameter discrepancy principle for combined projection and Tikhonov regularization of ill-posed problems
Convergence Characteristics of Methods of Regularization Estimators for Nonlinear Operator Equations
DISCRETIZATION ERROR ANALYSIS FOR TIKHONOV REGULARIZATION
Nonparametric statistical inverse problems
Spectral Algorithms for Supervised Learning
Remarks on Inequalities for Large Deviation Probabilities
Practical Approximate Solutions to Linear Operator Equations When the Data are Noisy
Comparisons of parameter choice methods for regularization with discrete noisy data
V-cycle convergence of some multigrid methods for ill-posed problems
Regularization by projection with a posteriori discretization level choice for linear and nonlinear ill-posed problems
High-Dimensional Probability
Statistical Inverse Estimation in Hilbert Scales
Self-regularization of projection methods with a posteriori discretization level choice for severely ill-posed problems
Data driven regularization by projection
Learning theory of distributed spectral algorithms
Regularization by projection: Approximation theoretic aspects and distance functions
Regularization by projection in variable Hilbert scales
Consistency and rates of convergence of nonlinear Tikhonov regularization with random noise
An adaptive discretization for Tikhonov-Phillips regularization with a posteriori parameter selection
Convex regularization in statistical inverse learning problems

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