Adaptive novelty detection with false discovery rate guarantee Page 1 The Annals of Statistics
2024, Vol. 52, No. 1, 157–183 https://doi.org/10.1214/23-AOS2338 © Institute of Mathematical …

The aim is to detect novelties, namely Xis with Pi= P0. This task is illustrated in Figure 1 on a
classical image data set, where we want to detect hand-written digit '9's in the test sample …

Sparse tensors appear frequently in federated deep learning, either as a direct artifact of the
deep neural network's gradients, or as a result of an explicit sparsification process. Existing …

We develop a new class of distribution-free multiple testing rules for false discovery rate
(FDR) control under general dependence. A key element in our proposal is a symmetrized …

Exploiting spatial patterns in large-scale multiple testing promises to improve both power
and interpretability of false discovery rate (FDR) analyses. This article develops a new class …

A fundamental task in the analysis of data sets with many variables is screening for
associations. This can be cast as a multiple testing task, where the objective is achieving …

Abstract The Phenome-Wide Association Study (PheWAS) is increasingly used to broadly
screen for potential treatment effects, eg, IL6R variant as a proxy for IL6R antagonists. This …

An important limitation of standard multiple testing procedures is that the null distribution
should be known. Here, we consider a null distribution-free approach for multiple testing in …

The use of weights provides an effective strategy to incorporate prior domain knowledge in
large-scale inference. This article studies weighted multiple testing in a decision-theoretical …

We investigate fairness in classification, where automated decisions are made for
individuals from different protected groups. In high-consequence scenarios, decision errors …