Machine learning meets false discovery rate

Classical false discovery rate (FDR) controlling procedures offer strong and interpretable guarantees but often lack flexibility to work with complex data. By contrast, machine learning-based classification algorithms have superior performances on modern datasets but typically fall short of error-controlling guarantees. In this paper, we make these two meet by introducing a new adaptive novelty detection procedure with FDR control, called AdaDetect. We illustrate our approach with classical real-world datasets, for which random forest and neural network versions of AdaDetect are particularly efficient.