mlsanityData to 14 Oct 2026

Unsupervised Domain Adaptation for Binary Classification with an Unobservable Source Subpopulation

arXiv 2509.20587 ↗First posted 24 Sep 2025stat.ML cs.LG stat.MEcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 40 of its 58 references that are in the map. First posted on arXiv 24 Sep 2025.

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References · 40

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28 more
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