  Controlling false discovery rate (FDR) while leveraging the side information
of multiple hypothesis testing is an emerging research topic in modern data
science. Existing methods rely on the test-level covariates while ignoring
possible hierarchy among the covariates. This strategy may not be optimal for
complex large-scale problems, where hierarchical information often exists among
those test-level covariates. We propose NeurT-FDR which boosts statistical
power and controls FDR for multiple hypothesis testing while leveraging the
hierarchy among test-level covariates. Our method parametrizes the test-level
covariates as a neural network and adjusts the feature hierarchy through a
regression framework, which enables flexible handling of high-dimensional
features as well as efficient end-to-end optimization. We show that NeurT-FDR
has strong FDR guarantees and makes substantially more discoveries in synthetic
and real datasets compared to competitive baselines.
