
The degree of illposedness of composite linear illposed problems with focus on the impact of the noncompact Hausdorff moment operator
We consider compact composite linear operators in Hilbert space, where t...
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Designing truncated priors for direct and inverse Bayesian problems
The Bayesian approach to inverse problems with functional unknowns, has ...
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Nonlinear Tikhonov regularization in Hilbert scales with oversmoothing penalty: inspecting balancing principles
The analysis of Tikhonov regularization for nonlinear illposed equation...
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Inverse learning in Hilbert scales
We study the linear illposed inverse problem with noisy data in the sta...
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Regularization of linear illposed problems involving multiplication operators
We study regularization of illposed equations involving multiplication ...
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Lepskii Principle in Supervised Learning
In the setting of supervised learning using reproducing kernel methods, ...
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Convergence analysis of Tikhonov regularization for nonlinear statistical inverse learning problems
We study a nonlinear statistical inverse learning problem, where we obs...
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Analysis of regularized Nyström subsampling for regression functions of low smoothness
This paper studies a Nyström type subsampling approach to large kernel l...
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Bayesian inverse problems with noncommuting operators
The Bayesian approach to illposed operator equations in Hilbert space r...
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Peter Mathé
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