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Inverse learning in Hilbert scales
We study the linear ill-posed inverse problem with noisy data in the statistical learning setting.
Convergence analysis of Tikhonov regularization for non-linear statistical inverse learning problems
We study a non-linear statistical inverse learning problem, where we observe the noisy image of a quantity through a non-linear operator at some random design points.
Analysis of regularized Nyström subsampling for regression functions of low smoothness
This paper studies a Nystr\"om type subsampling approach to large kernel learning methods in the misspecified case, where the target function is not assumed to belong to the reproducing kernel Hilbert space generated by the underlying kernel.
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