3 papers
math.ST2026
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…
stat.ML2025
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…
cs.LG2025
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…