用伪标签提升无监督域适应性能，解决数据分布差异问题。
Pseudo-Labeling for Unsupervised Domain Adaptation with Kernel GLMs
- 通过分批次源数据构建伪标签生成模型，实现目标域预测优化。
- 在真实与合成数据上，显著优于仅使用源数据的基线方法。
- 适用于存在协变量偏移的回归与分类任务，尤其适合小样本场景。
我们提出一种基于核广义线性模型（Kernel GLMs）的无监督域适应框架，处理协变量偏移问题，涵盖核化线性、逻辑与泊松回归并带岭正则化。目标是在源域标签数据和目标域无标签数据基础上，最小化目标域预测误差，尽管两者协变量分布不同。将源域标注数据分为两批：一批用于训练候选模型族，另一批用于构建插补模型以生成目标域伪标签，从而实现稳健的模型选择。我们建立了非渐近过失风险界，通过“有效标注样本量”量化适应性能，显式考虑未知的协变量偏移。在合成与真实数据集上的实验表明，该方法持续优于仅使用源数据的基线方法。
原文摘要 · Abstract (English)
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.
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