12 citations · 58 across the 17 of their papers we have counts for
19 papers
Towards regularized learning from functional data with covariate shift
Markus Holzleitner, Sergiy Pereverzyev, Sergei V. Pereverzyev +2
This paper investigates a general regularization framework for unsupervised domain adaptation in vector-valued regression under the covariate shift assumption, utilizing vector-val…
Multiparameter regularization and aggregation in the context of polynomial functional regression
Elke R. Gizewski, Markus Holzleitner, Lukas Mayer-Suess +2
Most of the recent results in polynomial functional regression have been focused on an in-depth exploration of single-parameter regularization schemes. In contrast, in this study w…
Overcoming Saturation in Density Ratio Estimation by Iterated Regularization
Lukas Gruber, Markus Holzleitner, Johannes Lehner +2
Estimating the ratio of two probability densities from finitely many samples, is a central task in machine learning and statistics. In this work, we show that a large class of kern…
Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators
Benedikt Alkin, Andreas Fürst, Simon Schmid +3
Neural operators, serving as physics surrogate models, have recently gained increased interest. With ever increasing problem complexity, the natural question arises: what is an eff…
SymbolicAI: A framework for logic-based approaches combining generative models and solvers
Marius-Constantin Dinu, Claudiu Leoveanu-Condrei, Markus Holzleitner +2
We introduce SymbolicAI, a versatile and modular framework employing a logic-based approach to concept learning and flow management in generative processes. SymbolicAI enables the…
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…