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arXiv · 2302.04724

Domain Generalization by Functional Regression

Markus Holzleitner·Sergei V. Pereverzyev·Werner Zellinger
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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.

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BibTeXRIS

Markus Holzleitner, Sergei V. Pereverzyev, Werner Zellinger. 2023-02-09. Domain Generalization by Functional Regression. https://doi.org/10.1080/01630563.2024.2320663

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