# NeurT-FDR: Controlling FDR by Incorporating Feature Hierarchy

arXiv: https://arxiv.org/abs/2101.09809 · HTML: https://mlsanity.com/paper/2101.09809/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 24 Jan 2021.

## Builds on (most cited first, 3 of 3)

- [NeuralFDR: Learning Discovery Thresholds from Hypothesis Features](https://mlsanity.com/paper/1711.01312/) (2017)
- [False discovery rate regression: an application to neural synchrony detection in primary visual cortex](https://mlsanity.com/paper/1307.3495/) (2013)
- [Black Box FDR](https://mlsanity.com/paper/1806.03143/) (2018)

## Built on by (most cited first, 1 of 1)

- [DART2: a robust multiple testing method to smartly leverage helpful or misleading ancillary information](https://mlsanity.com/paper/2409.03618/) (2024)

## Abstract (arXiv)

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.