相关论文: Learning a regression function via Tikhonov regula…
The paper has been withdrawn.
This paper has been withdrawn.
This paper has been withdrawn due to an error, and no further revisions will be made.
This paper has been withdrawn due to a crucial error in the proof of the main theorem
This paper has been withdrawn by the authors.
Tikhonov regularization is studied in the case of linear pseudodifferential operator as the forward map and additive white Gaussian noise as the measurement error. The measurement model for an unknown function $u(x)$ is \begin{eqnarray*}…
We investigate a Tikhonov regularization scheme specifically tailored for shallow neural networks within the context of solving a classic inverse problem: approximating an unknown function and its derivatives within a unit cubic domain…
This paper deals with an inertial proximal algorithm that contains a Tikhonov regularization term, in connection to the minimization problem of a convex lower semicontinuous function $f$. We show that for appropriate Tikhonov regularization…
This paper proposes a unified framework for the investigation of constrained learning theory in reflexive Banach spaces of features via regularized empirical risk minimization. The focus is placed on Tikhonov-like regularization with…
Although the results are correct, it was pointed out that the results follow from some previously known results. Accordingly, this version of the paper is withdrawn by the authors.
This paper has been withdrawn by the authors due to a problem with *efficiently* predicting the large fourier coefficients. It is being reworked and will be resubmitted in the near future.
This paper has been withdrawn by the authors
This paper has been withdrawn by the author. This draft is withdrawn for its poor quality in english, unfortunately produced by the author when he was just starting his science route. Look at the ICML version instead:…
This paper has been withdrawn by the author.
This paper has been withdrawn because there is a fundamental error in the computations; with the right computational scheme it seems to be just a version of the Jones polynomial
This paper has been withdrawn by the author due to a crucial error in the submission action.
This paper has been withdrawn.
The paper has been withdrawn due to numerical error.
This article addresses the challenge of learning effective regularizers for linear inverse problems. We analyze and compare several types of learned variational regularization against the theoretical benchmark of the optimal affine…
The present article studies the minimization of convex, L-smooth functions defined on a separable real Hilbert space. We analyze regularized stochastic gradient descent (reg-SGD), a variant of stochastic gradient descent that uses a…