Pseudo-Labeling for Unsupervised Domain Adaptation with Kernel GLMs
We propose a principled framework for unsupervised domain adaptation under covariate shift in kernel Generalized Linear Models (GLMs), encompassing kernelized linear, logistic, and Poisson regression with ridge regularization. Our goal is to minimize prediction error in the target domain by leveraging labeled source data and unlabeled target data, despite differences in covariate distributions. We partition the labeled source data into two batches: one for training a family of candidate models, and the other for building an imputation model. This imputation model generates pseudo-labels for the target data, enabling robust model selection. We establish non-asymptotic excess-risk bounds th...
| Authors | Nathan Weill, Kaizheng Wang |
|---|---|
| Primary category | stat.ML |
| Published | 19 March 2026 |
| Last updated at source | 19 March 2026 |
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