Generalized Distribution-Free Semi-Supervised Learning with Risk Rewrite

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[Submitted on 11 Jul 2026]

Title:Generalized Distribution-Free Semi-Supervised Learning with Risk Rewrite

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Abstract:Typical semi-supervised learning (SSL) methods rely on distributional assumptions, and their performance degrades when these are violated. While PNU learning, a risk rewriting method, offers a distribution-free alternative, it is restricted to binary classification and its variance optimality remains unclear. In this paper, we propose a generalized framework that constructs unbiased risk estimators using linear combinations of component risks, subsuming PNU learning and extending to multiclass classification. We derive the minimum achievable variance, demonstrating our estimator can attain lower variance than PNU in asymmetric loss scenarios. Furthermore, we establish a generalization bound directly linking this variance reduction to improved learning performance. Based on these theoretical insights, we introduce two practical SSL methods that empirically match or outperform existing approaches on binary and multiclass benchmarks.

Comments: Accepted to The Conference on Uncertainty in Artificial Intelligence (UAI) 2026

Subjects:

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

Cite as: arXiv:2607.11947 [cs.LG]

(or arXiv:2607.11947v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.11947

arXiv-issued DOI via DataCite

Submission history

From: Yushi Hirose [view email] [v1] Sat, 11 Jul 2026 13:51:56 UTC (324 KB)

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