arXiv:2605.28533v1 Announce Type: new Abstract: We introduce a testing-by-betting framework that leverages predictions on unlabeled data to enhance the power of sequential hypothesis testing. Given limited samples from the joint distribution of $(X,Y)$, and additional unlabeled samples from the marginal of $X$, we ask how unlabeled data can be used to hypothesize about the distribution of $Y$, and the conditional distribution of $Y\mid X$. We introduce an e-statistic and use it to construct a sequential test. Under standard distributional assumptions -- label shift or concept shift -- we estab
The continuous growth of unlabeled data sources has accelerated research into methods that leverage this abundance for more powerful statistical inference and machine learning applications.
This research introduces a novel framework that improves the efficiency and power of hypothesis testing by intelligently utilizing unlabeled data, which is often far more plentiful than labeled data.
The ability to perform more robust and sequential hypothesis testing with less reliance on scarce labeled data could accelerate scientific discovery, AI model development, and real-time decision-making in various fields.
- · AI/ML researchers
- · Data scientists
- · Biotech/Medtech (for faster drug trials/diagnostics)
- · Any industry with abundant unlabeled data
- · Traditional statistical methods reliant solely on labeled data
More powerful and efficient statistical tests will be developed and deployed in diverse applications.
Faster iteration cycles in scientific research and AI development due to reduced necessity for extensive data labeling.
New product categories and services could emerge that are built around extremely data-efficient statistical inference, potentially democratizing advanced analytics.
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Read at arXiv cs.LG