发表机构
Trent University（特伦特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究模型发布门控的严格程度设计，提出两类别潜在因子模型量化测试相关性的代价，证明最宽松达标门控最优，并给出基于精确二项边界的验证程序。
AI 中文摘要
在机器学习模型发布之前，通常需要通过一系列自动化测试。要求所有测试都通过看似安全，但这可能会拒绝许多本可以为用户提供良好服务的模型，并且它并未说明通过测试的模型实际上有多可信。我们将发布门控视为一个设计问题：选择模型必须通过的测试数量，使得通过门控的模型达到规定的可靠性目标，同时尽可能保留更多的好模型。一个两类别潜在因子模型使两种成本变得明确，并将每个计算简化为一个一维积分。我们证明，当两个类别共享相同的潜在相关性时，更严格的门控总是提高可靠性，因此保留最多好模型的门控是仍能满足目标的最宽松门控。在全部通过门控下，任何低于完美的可靠性目标都可以在模型内实现，但随着测试套件的增长，保留的好模型比例趋向于零。测试之间的相关性决定了代价。在一种配置中，99%的目标需要8个独立测试，但在潜在相关性为0.3时需要74个测试，在0.5时需要5,182个测试，此时门控保留的好模型不到十分之一。我们还提供了一种基于精确二项边界的验证程序，即使门控是从固定的候选列表中选出的，也能从带标签的数据中认证门控。
英文摘要
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 keeps the most good models is the most lenient one that still meets the target. Under pass-all gating, any reliability target short of perfection is attainable within the model, but the share of good models kept tends to zero as the suite grows. Correlation between tests sets the price. In one configuration, a 99 percent target needs 8 independent tests, but 74 tests at a latent correlation of 0.3 and 5,182 at 0.5, where the gate keeps fewer than one good model in ten. We also give a validation procedure, built on exact binomial bounds, that certifies a gate from labelled data even when the gate is chosen from a fixed shortlist.
Comments6 pages, 1 figure, 4 tables. Submitted to ACDSA 2027
