From matrix inversion to constraints: provably tighter confidence regions for importance weights in label shift
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arXiv:2609.14802v1 Announce Type: new Abstract: Importance weights are essential in domain adaptation under label shift, yet their utility is often undermined by the finite sample uncertainty associated with their estimation. Existing methods typically analyze this uncertainty through Gaussian elim…
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- 2026-09-15 04:00 · arXiv stat.ML
From matrix inversion to constraints: provably tighter confidence regions for importance weights in label shift