通过Conformal E-values实现FDR控制的去随机化新颖性检测
Derandomized Novelty Detection with FDR Control via Conformal E-values
AI总结：
提出利用conformal e-values替代p-values进行去随机化的新颖性检测，在有效聚合多次分析证据并控制FDR的同时减少随机性，且功效损失较小。
摘要：
Conformal inference提供了一种通用的无分布方法，能够严格校准任何机器学习算法在新颖性检测中的输出。尽管该方法具有诸多优势，但其存在随机化的局限性，即对相同数据进行两次分析可能产生不同结果，这可能阻碍对任何发现的解释。我们提出利用合适的conformal e-values代替p-values来量化统计显著性，从而使conformal inferences更加稳定。该解决方案能够有效地聚合对相同数据进行多次分析所收集的证据，同时可证明地控制false discovery rate。此外，我们表明，与标准conformal inference相比，所提方法能够在功效损失不大的情况下减少随机性，部分得益于一种基于从相同数据中仔细提取的额外辅助信息对conformal e-values进行加权的创新方法。使用合成数据和真实数据进行的模拟证实，该解决方案能够有效消除使用最先进替代技术获得的推断中的随机噪声，有时还能带来更高的功效。
英文摘要：
Conformal inference provides a general distribution-free method to rigorously calibrate the output of any machine learning algorithm for novelty detection. While this approach has many strengths, it has the limitation of being randomized, in the sense that it may lead to different results when analyzing twice the same data, and this can hinder the interpretation of any findings. We propose to make conformal inferences more stable by leveraging suitable conformal e-values instead of p-values to quantify statistical significance. This solution allows the evidence gathered from multiple analyses of the same data to be aggregated effectively while provably controlling the false discovery rate. Further, we show that the proposed method can reduce randomness without much loss of power compared to standard conformal inference, partly thanks to an innovative way of weighting conformal e-values based on additional side information carefully extracted from the same data. Simulations with synthetic and real data confirm this solution can be effective at eliminating random noise in the inferences obtained with state-of-the-art alternative techniques, sometimes also leading to higher power.
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