SOURCE-LINKED INTELLIGENCE
Exact Risk-Complexity Laws for Projective Boundaries in Scenario Optimization and Distribution-Free Certification
Scenario optimization, conformal prediction, and related distribution-free certification methods use finite samples to construct decisions or prediction sets with violation-risk guarantees for fresh observations. In several classical settings, the conditional violation risk follows an exact beta law, whose tail has a beta-binomial representation and whose parameter is a support, calibration, or compression dimension. This paper identifies the deterministic boundary mechanism behind these formulas and derives the corresponding law when the observed boundary size is random. A decision rule is re
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-01T14:58:38.000Z
First collected: 2026-09-21T06:01:56.170Z. This is not the publication date.