Importance Weighting for Unlabeled-unlabeled Learning under Distribution Shift
Updated 41 min ago · first seen 11 Sept 2026
paper_01M294FNY5NQXKH0YY1B72XCA3
- Published
- 11 Sept 2026
- T1 · 41 min ago
- arXiv
- 2609.10994
- T1 · 41 min ago
- Category
- cs.LG
- T1 · 41 min ago
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.
Authors 6
Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi, Taishi Nishiyama, Kazuki Adachi, Yasuhiro Fujiwara
Specification
- Official page
Source:arXiv (Atom API + RSS)T1observed 41 min agohigh
- Arxiv announce type
- new
Source:arXiv (Atom API + RSS)T1observed 41 min agohigh
- arXiv id
- 2609.10994
Source:arXiv (Atom API + RSS)T1observed 41 min agohigh
- Categories
- cs.LG, cs.AI, stat.ML
Source:arXiv (Atom API + RSS)T1observed 41 min agohigh
Source:arXiv (Atom API + RSS)T1observed 41 min agohigh
- Primary category
- cs.LG
Source:arXiv (Atom API + RSS)T1observed 41 min agohigh
- Published
- 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 41 min agohigh
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9
Source tiers
T19
Freshest observation
41 min ago
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Claim history · Abstract
Abstractabstract1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| 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. | → current | current | arXiv (Atom API + RSS)T1 | high | deterministic |
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
New paper: Importance Weighting for Unlabeled-unlabeled Learning under Distribution Shift
arxiv
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.LG | feed | T1· Official | 41 min ago | 1 |
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.