Domain Generalization by Functional Regression

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.

References in corpus (2)

• Domain Generalization via Invariant Feature Representation
• Domain Generalization by Marginal Transfer Learning

Cited by in corpus (1)

• Generalization Analysis of Transformers in Distribution Regression