AI RESEARCH
Pseudo-Labeling for Unsupervised Domain Adaptation with Kernel GLMs
arXiv CS.LG
•
ArXi:2603.19422v1 Announce Type: cross 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.