arXiv:2604.11305v3 Announce Type: replace Abstract: Conformal selection (CS) uses calibration data to identify test inputs whose unobserved outcomes are likely to satisfy a pre-specified minimal quality requirement, while controlling the false discovery rate (FDR). Existing methods fix the target FDR level before observing data, which prevents the user from adapting the balance between number of selected test inputs and FDR to downstream needs and constraints based on the available data. For example, in genomics or neuroimaging, researchers often inspect the distribution of test statistics, an
The paper was published on the arXiv during a period of rapid advancement in AI/ML methodology, suggesting continuous refinement of statistical techniques for complex data analysis.
Improving false discovery rate control in post-hoc analysis enhances the reliability and interpretability of results in fields like genomics and neuroimaging, crucial for scientific discovery and AI model validation.
Users can now dynamically adjust the balance between selected test inputs and false discovery rate post-experiment, adapting to specific needs and data characteristics rather than being bound by pre-set levels.
- · AI/ML researchers
- · Genomics research
- · Neuroimaging
- · Statistical method developers
- · Fixed-FDR methodologies
- · Studies with rigid pre-analysis plans
More robust and flexible control over false discoveries in complex data analysis, particularly for AI applications.
Accelerated and more reliable scientific discovery in data-rich fields, leading to faster progress in AI-driven science.
Increased trust in AI-driven scientific conclusions and medical diagnostics due to enhanced statistical rigor and adaptability.
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Read at arXiv cs.LG