Importance Weighting for Unlabeled-unlabeled Learning under Distribution Shift
Quick summary
arXiv:2609.10994v1 Announce Type: cross 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 adapt
Key takeaways
- arXiv:2609.10994v1 Announce Type: cross 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.
Why it matters
“Importance Weighting for Unlabeled-unlabeled Learning under Distribution Shift” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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