117 citations · 133 across the 8 of their papers we have counts for
9 papers
On regularized polynomial functional regression
Markus Holzleitner, Sergei Pereverzyev
This article offers a comprehensive treatment of polynomial functional regression, culminating in the establishment of a novel finite sample bound. This bound encompasses various a…
On regularized Radon-Nikodym differentiation
Duc Hoan Nguyen, Werner Zellinger, Sergei V. Pereverzyev
We discuss the problem of estimating Radon-Nikodym derivatives. This problem appears in various applications, such as covariate shift adaptation, likelihood-ratio testing, mutual i…
General regularization in covariate shift adaptation
Duc Hoan Nguyen, Sergei V. Pereverzyev, Werner Zellinger
Sample reweighting is one of the most widely used methods for correcting the error of least squares learning algorithms in reproducing kernel Hilbert spaces (RKHS), that is caused…
Adaptive learning of density ratios in RKHS
Werner Zellinger, Stefan Kindermann, Sergei V. Pereverzyev
Estimating the ratio of two probability densities from finitely many observations of the densities is a central problem in machine learning and statistics with applications in two-…
Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation
Marius-Constantin Dinu, Markus Holzleitner, Maximilian Beck +7
We study the problem of choosing algorithm hyper-parameters in unsupervised domain adaptation, i.e., with labeled data in a source domain and unlabeled data in a target domain, dra…
Domain Generalization by Functional Regression
Markus Holzleitner, Sergei V. Pereverzyev, Werner Zellinger
The problem of domain generalization is to learn, given data from different source distributions, a model that can be expected to generalize well on new target distributions which…
A deep learning approach to diabetic blood glucose prediction
H. N. Mhaskar, S. V. Pereverzyev, M. D. van der Walt
We consider the question of 30-minute prediction of blood glucose levels measured by continuous glucose monitoring devices, using clinical data. While most studies of this nature d…
Parameter choice strategies for least-squares approximation of noisy smooth functions on the sphere
Sergei. V. Pereverzyev, Ian. H. Sloan, Pavlo Tkachenko
We consider a polynomial reconstruction of smooth functions from their noisy values at discrete nodes on the unit sphere by a variant of the regularized least-squares method of An…
Two-parameter regularization of ill-posed spherical pseudo-differential equations in the space of continuous functions
Hui Cao, Sergei V. Pereverzyev, Ian H. Sloan +1
In this paper, a two-step regularization method is used to solve an ill-posed spherical pseudo-differential equation in the presence of noisy data. For the first step of regulariza…