117 citations · 133 across the 8 of their papers we have counts for
6 papers · 1 filter
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…