[2603.19422] Pseudo-Labeling for Unsupervised Domain Adaptation with Kernel GLMs

Statistics > Machine Learning arXiv:2603.19422 (stat) [Submitted on 19 Mar 2026] Title:Pseudo-Labeling for Unsupervised Domain Adaptation with Kernel GLMs Authors:Nathan Weill, Kaizheng Wang View a PDF of the paper titled Pseudo-Labeling for Unsupervised Domain Adaptation with Kernel GLMs, by Nathan Weill and Kaizheng Wang View PDF HTML (experimental) Abstract: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 that characterize adaptation performance through an "effective labeled sample size", explicitly accounting for the unknown covariate shift. Experiments on synthetic and real datasets demonstrate consistent performance gains over source-only baselines. Comments: Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST) Cite as: arXiv:2603.19422 [stat.ML]   (or arXiv:2603.19422v1 [stat.ML] for this versi...