Related papers: Learning a regression function via Tikhonov regula…

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The present paper is focused on the problem of recovering the Radon-Nikodym derivative under the big data assumption. To address the above problem, we design an algorithm that is a combination of the Nystr\"om subsampling and the standard…

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This paper has been withdrawn by the author(s), due to a crucial error in eq. 6.

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This paper has been withdrawn by the author due to an error in the proof of Theorem 1.

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This paper presents an error analysis of classical and learned Tikhonov regularization schemes for inverse problems. We first demonstrate, both theoretically and numerically, that using a fixed regularization parameter across varying noise…

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Learning convolution kernels in operators from data arises in numerous applications and represents an ill-posed inverse problem of broad interest. With scant prior information, kernel methods offer a natural nonparametric approach with…

A novel algorithm was recently presented to utilize emerging time dependent probability density data to extract molecular potential energy surfaces. This paper builds on the previous work and seeks to enhance the capabilities of the…

This paper has been withdrawn by the author due to rewritting and skipping crucial sign errors.

This paper has been withdrawn by the author because there are some typos in proofs.