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汇聚集体智慧：利用多来源决策序列的融合学习AI 解读

Summary We introduce an integrative ranking and thresholding framework for fusing evidence from multiple testing procedures. The key innovation is a method that transforms binary testing decisions into compound $ e $-values, enabling the combination of findings across diverse data sources or studies. We demonstrate that our new framework ensures overall false discovery rate control, provided that the individual studies maintain their respective false discovery rate levels.…

利用辅助信息的自适应稀疏估计AI 解读

The article considers the problem of estimating a high-dimensional sparse parameter in the presence of side information that encodes the sparsity structure. We develop a general framework that involves first using an auxiliary sequence to capture the side information, and then incorporating the auxiliary sequence in inference to reduce the estimation risk. The proposed method, which carries out adaptive Stein’s unbiased risk estimate-thresholding using side information…