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#label-shift

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papersTODAY 04:00 UTC

Paper proposes tighter confidence regions for importance weights in label shift

A new arXiv preprint addresses how finite-sample uncertainty degrades importance weights used for domain adaptation under label shift. Existing work often relies on Gaussian approximations, while this paper derives confidence regions that convert the problem into a matrix inversion and constraint formulation, yielding provably tighter bounds. The result is intended to make weight-based adaptation more reliable when sample sizes are limited.

label-shift/domain-adaptation/uncertainty/arxiv