# Adaptive novelty detection with false discovery rate guarantee

arXiv: https://arxiv.org/abs/2208.06685 · HTML: https://mlsanity.com/paper/2208.06685/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 13 Aug 2022.

## Builds on (most cited first, 7 of 7)

- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [Testing for Outliers with Conformal p-values](https://mlsanity.com/paper/2104.08279/) (2021)
- [Convergence rates of deep ReLU networks for multiclass classification](https://mlsanity.com/paper/2108.00969/) (2021)
- [Global and Simultaneous Hypothesis Testing for High-Dimensional Logistic Regression Models](https://mlsanity.com/paper/1805.06970/) (2018)
- [False Discovery Rate Control via Debiased Lasso](https://mlsanity.com/paper/1803.04464/) (2018)
- [Semi-supervised multiple testing](https://mlsanity.com/paper/2106.13501/) (2021)
- [Smoothed Nested Testing on Directed Acyclic Graphs](https://mlsanity.com/paper/1911.09200/) (2019)

## Built on by (most cited first, 15 of 15)

- [Conformal link prediction for false discovery rate control](https://mlsanity.com/paper/2306.14693/) (2023)
- [Selecting informative conformal prediction sets with false coverage rate control](https://mlsanity.com/paper/2403.12295/) (2024)
- [Powerful batch conformal prediction for classification](https://mlsanity.com/paper/2411.02239/) (2024)
- [Conformal Inference for Open-Set and Imbalanced Classification](https://mlsanity.com/paper/2510.13037/) (2025)
- [Model-agnostic Selective Labeling with Provable Statistical Guarantees](https://mlsanity.com/paper/2510.14581/) (2025)
- [Causal Responder Detection](https://mlsanity.com/paper/2406.17571/) (2024)
- [Conformal Data Contamination Tests for In-distribution Data Acquisition](https://mlsanity.com/paper/2507.13835/) (2025)
- [Elements of Conformal Prediction](https://mlsanity.com/paper/2603.23923/) (2026)
- [On the Adversarial Robustness of Learning-based Conformal Novelty Detection](https://mlsanity.com/paper/2510.00463/) (2025)
- [Reliable conformal novelty detection at the decision boundary](https://mlsanity.com/paper/2601.02610/) (2026)
- [Decentralized Conformal Novelty Detection via Quantized Model Exchange](https://mlsanity.com/paper/2605.08263/) (2026)
- [Everywhere Valid Bounds on False Discovery Proportions in Conformal Inference](https://mlsanity.com/paper/2605.20726/) (2026)
- [Conformal calibration and look-elsewhere effect in anomaly detection for new-physics searches](https://mlsanity.com/paper/2606.13780/) (2026)
- [Self-Poisoning in Adaptive Out-of-Distribution Detection: A Sharp-Threshold Theory and Certified Label-Free Calibration](https://mlsanity.com/paper/2607.21673/) (2026)
- [Neyman-Pearson Classification with Indecisions: Simultaneous Control of Selective Type I and Type II Errors](https://mlsanity.com/paper/2610.04233/) (2026)

## Abstract (arXiv)

This paper studies the semi-supervised novelty detection problem where a set of "typical" measurements is available to the researcher. Motivated by recent advances in multiple testing and conformal inference, we propose AdaDetect, a flexible method that is able to wrap around any probabilistic classification algorithm and control the false discovery rate (FDR) on detected novelties in finite samples without any distributional assumption other than exchangeability. In contrast to classical FDR-controlling procedures that are often committed to a pre-specified p-value function, AdaDetect learns the transformation in a data-adaptive manner to focus the power on the directions that distinguish between inliers and outliers. Inspired by the multiple testing literature, we further propose variants of AdaDetect that are adaptive to the proportion of nulls while maintaining the finite-sample FDR control. The methods are illustrated on synthetic datasets and real-world datasets, including an application in astrophysics.