Importance Weighting for Unlabeled-unlabeled Learning under Distribution Shift
Abstract
Unlabeled-unlabeled (UU) learning allows us to learn a binary classifier from two sets of unlabeled data with different class-priors. It is a general framework because it includes a wide variety of supervised learning such as positive-unlabeled (PU) learning, noisy label learning, and similarity-based learning. Existing UU learning assumes that the test and training distributions have the same class-conditional densities. However, this assumption rarely holds in practice due to distribution shifts. This paper proposes a distribution shift adaptation method for UU learning that uses UU data in the training distribution and a few UU data in the test distribution. The proposed method is based on the importance weighting, which minimizes the test risk by using training data with estimated importance weights. Although existing importance weighting methods cannot handle UU data, we show that it can be done in a principled manner. Thanks to the generality of UU learning, our method can handle various learning problems such as PU and noisy label learning under distribution shift within a single framework while existing methods are usually tailored to a specific problem. Moreover, it does not require any assumption of the shift types such as covariate shift. We experimentally demonstrate the effectiveness of the proposed method with real-world datasets.
Related
- Latent-Space Importance Weighting under Covariate Shift
- Learning under Target Shift: Optimal Density Ratio Estimation and Importance-Weighted Regression
- From matrix inversion to constraints: provably tighter confidence regions for importance weights in label shift
- Asymptotically Exact Negative Guidance of Diffusion Models via Positive-Unlabeled Learning
- Fraud Detection via Bayesian Positive-Unlabeled Learning with Gaussian Processes and Multilayer Networks
BibTeX
@misc{kumagai2026importance,
title = {{Importance Weighting for Unlabeled-unlabeled Learning under Distribution Shift}},
author = {Atsutoshi Kumagai and Tomoharu Iwata and Hiroshi Takahashi and Taishi Nishiyama and Kazuki Adachi and Yasuhiro Fujiwara},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.10994},
eprint = {2609.10994},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.10994},
}
RIS
TY - UNPB
TI - Importance Weighting for Unlabeled-unlabeled Learning under Distribution Shift
AU - Atsutoshi Kumagai
AU - Tomoharu Iwata
AU - Hiroshi Takahashi
AU - Taishi Nishiyama
AU - Kazuki Adachi
AU - Yasuhiro Fujiwara
PY - 2026
DA - 2026/09/10
PB - arXiv
DO - 10.48550/arxiv.2609.10994
UR - https://arxiv.org/abs/2609.10994
AB - Unlabeled-unlabeled (UU) learning allows us to learn a binary classifier from two sets of unlabeled data with different class-priors. It is a general framework because it includes a wide variety of supervised learning such as positive-unlabeled (PU) learning, noisy label learning, and similarity-based learning. Existing UU learning assumes that the test and training distributions have the same class-conditional densities. However, this assumption rarely holds in practice due to distribution shifts. This paper proposes a distribution shift adaptation method for UU learning that uses UU data in the training distribution and a few UU data in the test distribution. The proposed method is based on the importance weighting, which minimizes the test risk by using training data with estimated importance weights. Although existing importance weighting methods cannot handle UU data, we show that it can be done in a principled manner. Thanks to the generality of UU learning, our method can handle various learning problems such as PU and noisy label learning under distribution shift within a single framework while existing methods are usually tailored to a specific problem. Moreover, it does not require any assumption of the shift types such as covariate shift. We experimentally demonstrate the effectiveness of the proposed method with real-world datasets.
ER -