Unsupervised Domain Adaptation for Binary Classification with an Unobservable Source Subpopulation
Year: 2025
Type: preprint
Abstract: We study an unsupervised domain adaptation problem where the source domain consists of subpopulations defined by the binary label $Y$ and a binary background (or environment) $A$. We focus on a challenging setting in which one such subpopulation in the source domain is unobservable. Naively ignoring this unobserved group can result in biased estimates and degraded predictive performance. Despite this structured missingness, we show that the prediction in the target domain can still be recovered. Specifically, we rigorously derive both background-specific and overall prediction models for the target domain. For practical implementation, we propose the distribution matching method to estimate the subpopulation proportions. We provide theoretical guarantees for the asymptotic behavior of our estimator, and establish an upper bound on the prediction error. Experiments on both synthetic and real-world datasets show that our method outperforms the naive benchmark that does not account for this unobservable source subpopulation.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1523888516, A Robust and Efficient Statistical Framework for Handling Missing-Not-At-Random Data in Patient Reported Outcomes and Beyond, A Robust and Efficient Statistical Framework for Handling Missing-Not-At-Random Data in Patient Reported Outcomes and Beyond, SLES: Foundations of Safety-Aware Learning in the Wild, Semiparametric Techniques for Data Exploitation across Heterogeneous Populations, G6098521345, G6108913550, G6602531639, CAREER: Foundations of Human-Centered Machine Learning in the Wild, G8876996369, CoPE II: Individualizing patient-reported outcomes in patient care for vocal fold paralysis in the clinic and in research
OpenAlex ID: W4414788051