Domain Generalization by Functional Regression
Machine Learning
2024-03-12 v2 Statistics Theory
Machine Learning
Statistics Theory
Abstract
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 are only seen through unlabeled samples. In this paper, we study domain generalization as a problem of functional regression. Our concept leads to a new algorithm for learning a linear operator from marginal distributions of inputs to the corresponding conditional distributions of outputs given inputs. Our algorithm allows a source distribution-dependent construction of reproducing kernel Hilbert spaces for prediction, and, satisfies finite sample error bounds for the idealized risk. Numerical implementations and source code are available.
Cite
@article{arxiv.2302.04724,
title = {Domain Generalization by Functional Regression},
author = {Markus Holzleitner and Sergei V. Pereverzyev and Werner Zellinger},
journal= {arXiv preprint arXiv:2302.04724},
year = {2024}
}