Standard multiple testing procedures are designed to report a list of discoveries, or suspected false null hypotheses, given the hypotheses' p-values or test scores. Recently there has been a growing interest in enhancing such procedures by combining additional information with the primary p-value or score. In line with this idea, we develop RESET (REScoring via Estimating and Training), which uses a unique data-splitting protocol that subsequently allows any semi-supervised learning approach to factor in the available side information while maintaining finite sample error rate control. Our practical implementation, RESET Ensemble, selects from an ensemble of classification algorithms so that it is compatible with a range of multiple testing scenarios without the need for the user to select the appropriate one. We apply RESET to both p-value and competition based multiple testing problems and show that RESET is (1) power-wise competitive, (2) fast compared to most tools and (3) able to uniquely achieve finite sample false discovery rate or false discovery exceedance control, depending on the user's preference.

翻译：标准的多重检验方法旨在根据假设的p值或检验分数报告一系列发现或疑似错误零假设。近年来，通过将附加信息与主要p值或分数相结合以增强此类方法的研究日益受到关注。基于这一思路，我们开发了RESET（通过估计与训练的重评分方法），该方法采用一种独特的数据分割协议，使得任何半监督学习方法在整合可用辅助信息的同时，仍能保持有限样本误差率控制。我们的实际实现方案——RESET集成——从分类算法集合中进行选择，从而使其兼容多种多重检验场景，无需用户手动选择合适算法。我们将RESET应用于基于p值和竞争的多重检验问题，结果表明RESET具有以下特点：（1）在统计功效方面具有竞争力；（2）相较于多数工具更为快速；（3）能够根据用户偏好，独特地实现有限样本错误发现率或错误发现超出量控制。