# A New Perspective on Pool-Based Active Classification and False-Discovery Control

arXiv: https://arxiv.org/abs/2008.06555 · HTML: https://mlsanity.com/paper/2008.06555/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2020.

## Built on by (most cited first, 9 of 9)

- [Active Learning with Safety Constraints](https://mlsanity.com/paper/2206.11183/) (2022)
- [Improved Algorithms for Agnostic Pool-based Active Classification](https://mlsanity.com/paper/2105.06499/) (2021)
- [A unified framework for bandit multiple testing](https://mlsanity.com/paper/2107.07322/) (2021)
- [Improved Algorithm for Deep Active Learning under Imbalance via Optimal Separation](https://mlsanity.com/paper/2312.09196/) (2023)
- [AHA: Human-Assisted Out-of-Distribution Generalization and Detection](https://mlsanity.com/paper/2410.08000/) (2024)
- [Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits](https://mlsanity.com/paper/2110.09133/) (2021)
- [Active Covering](https://mlsanity.com/paper/2106.02552/) (2021)
- [Multi-Metric Adaptive Experimental Design Under a Fixed Budget with Validation](https://mlsanity.com/paper/2506.03062/) (2025)
- [Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers](https://mlsanity.com/paper/2111.04915/) (2021)

## Abstract (arXiv)

In many scientific settings there is a need for adaptive experimental design to guide the process of identifying regions of the search space that contain as many true positives as possible subject to a low rate of false discoveries (i.e. false alarms). Such regions of the search space could differ drastically from a predicted set that minimizes 0/1 error and accurate identification could require very different sampling strategies. Like active learning for binary classification, this experimental design cannot be optimally chosen a priori, but rather the data must be taken sequentially and adaptively. However, unlike classification with 0/1 error, collecting data adaptively to find a set with high true positive rate and low false discovery rate (FDR) is not as well understood. In this paper we provide the first provably sample efficient adaptive algorithm for this problem. Along the way we highlight connections between classification, combinatorial bandits, and FDR control making contributions to each.