The Price of Correlated Tests: How Strict Should a Model Release Gate Be?
Before a machine learning model ships, it often has to pass a suite of automated tests. Requiring every test to pass looks safe, yet it can reject many models that would have served users well, and it does not say how trustworthy a passing model actually is. We treat the release gate as a design problem: choose how many tests a model must pass so that cleared models meet a stated reliability target, while keeping as many good models as possible. A two-class latent-factor model makes both costs explicit and reduces each calculation to a one-dimensional integral. We prove that when both classes share the same latent correlation, a stricter gate always raises reliability, so the gate that ke...
| Authors | Marco Pollanen |
|---|---|
| Primary category | stat.ML |
| Published | 1 October 2026 |
| Last updated at source | 1 October 2026 |
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