# Search Improves Label for Active Learning

arXiv: https://arxiv.org/abs/1602.07265 · HTML: https://mlsanity.com/paper/1602.07265/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2016.

## Built on by (most cited first, 4 of 4)

- [Exact Recovery of Clusters in Finite Metric Spaces Using Oracle Queries](https://mlsanity.com/paper/2102.00504/) (2021)
- [Machine Learning for Fraud Detection in E-Commerce: A Research Agenda](https://mlsanity.com/paper/2107.01979/) (2021)
- [AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets](https://mlsanity.com/paper/2404.05623/) (2024)
- [Active Learning of Classifiers with Label and Seed Queries](https://mlsanity.com/paper/2209.03996/) (2022)

## Abstract (arXiv)

We investigate active learning with access to two distinct oracles: Label (which is standard) and Search (which is not). The Search oracle models the situation where a human searches a database to seed or counterexample an existing solution. Search is stronger than Label while being natural to implement in many situations. We show that an algorithm using both oracles can provide exponentially large problem-dependent improvements over Label alone.