arXiv Machine Learning September 11, 2026 By Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi, Taishi Nishiyama, Kazuki Adachi, Yasuhiro Fujiwara

Importance Weighting for Unlabeled-unlabeled Learning under Distribution Shift

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This paper introduces a method for adapting unlabeled‑unlabeled (UU) learning to distribution shifts by applying importance weighting. The approach estimates weights from UU data in both training and test distributions to minimize test risk, enabling handling of various learning scenarios—including PU and noisy‑label learning—without assuming specific shift types. Experiments on real‑world datasets confirm the method’s effectiveness.

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