# Crowdsourcing Feature Discovery via Adaptively Chosen Comparisons

arXiv: https://arxiv.org/abs/1504.00064 · HTML: https://mlsanity.com/paper/1504.00064/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2015.

## Built on by (most cited first, 5 of 5)

- [The future of human-AI collaboration: a taxonomy of design knowledge for hybrid intelligence systems](https://mlsanity.com/paper/2105.03354/) (2021)
- [Optimal Sample Complexity of Contrastive Learning](https://mlsanity.com/paper/2312.00379/) (2023)
- [Knowledge Learning with Crowdsourcing: A Brief Review and Systematic Perspective](https://mlsanity.com/paper/2206.09315/) (2022)
- [Improved Robust Algorithms for Learning with Discriminative Feature Feedback](https://mlsanity.com/paper/2209.03753/) (2022)
- [Discriminative Feature Feedback with General Teacher Classes](https://mlsanity.com/paper/2510.07245/) (2025)

## Abstract (arXiv)

We introduce an unsupervised approach to efficiently discover the underlying features in a data set via crowdsourcing. Our queries ask crowd members to articulate a feature common to two out of three displayed examples. In addition we also ask the crowd to provide binary labels to the remaining examples based on the discovered features. The triples are chosen adaptively based on the labels of the previously discovered features on the data set. In two natural models of features, hierarchical and independent, we show that a simple adaptive algorithm, using "two-out-of-three" similarity queries, recovers all features with less labor than any nonadaptive algorithm. Experimental results validate the theoretical findings.