Reviews: NeuralFDR: Learning Discovery Thresholds from Hypothesis Features

The paper describes a new method for FDR control for p-values with additional information. For each hypothesis, there is a p-value p_i, and there is also a feature vector X_i. The method learns the optimal threshold for each hypothesis, as a function of the features vector X_i. The idea seems interesting and novel, and overall the paper is explained quite clearly. In several simulated and real data example, the authors show that their method can use the additional information to increase the number of rejections, for a given FDR control threshold. It seems to me to be important that the X_i's were not used to calculate the P_i, otherwise we get a problem of circularity.