1. Domain Generalization by Functional Regression.
- Author
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Holzleitner, Markus, Pereverzyev, Sergei V., and Zellinger, Werner
- Subjects
- *
GENERALIZATION , *LINEAR operators , *SAMPLING errors , *SOURCE code - 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 available1. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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