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Importance Weighting for Unlabeled-unlabeled Learning under Distribution Shift

This story is from 2026-09-11. It is preserved in the archive; the latest stories are on the live feed.

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)…

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  1. 2026-09-11 04:00 · arXiv stat.ML
    Importance Weighting for Unlabeled-unlabeled Learning under Distribution Shift

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