NeuralFDR learns optimal discovery thresholds from hypothesis features.
problem Maximizing useful discoveries while controlling false positives in rich datasets.
method Proposes NeuralFDR, a neural network that learns a discovery threshold as a function of hypothesis features.
result Demonstrates substantially more discoveries and interpretable learned thresholds in synthetic and real datasets.