Anytime-Valid Confirmation of Label-Shift Corrections
In small-batch scientific deployments, labeled target outcomes may be too scarce for reliable shift estimation even when unlabeled target inputs are available. We address the complementary setting where the practitioner has a pre-specified label-shift correction from domain knowledge and asks whether incoming labeled outcomes support it. We show that the per-observation likelihood ratio between a label-shift-corrected predictive and the source predictive is a conditional e-value, so its running product is a nonnegative martingale and Ville's inequality yields an anytime-valid confirmation rule. The log martingale equals the cumulative negative log-predictive density (NLPD) gap between the...
| Authors | Seungjin Choi |
|---|---|
| Primary category | stat.ML |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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