# Regularized Minimax Conditional Entropy for Crowdsourcing

arXiv: https://arxiv.org/abs/1503.07240 · HTML: https://mlsanity.com/paper/1503.07240/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2015.

## Built on by (most cited first, 6 of 6)

- [Label noise in segmentation networks : mitigation must deal with bias](https://mlsanity.com/paper/2107.02189/) (2021)
- [Crowdsourcing via Annotator Co-occurrence Imputation and Provable Symmetric Nonnegative Matrix Factorization](https://mlsanity.com/paper/2106.07193/) (2021)
- [A Worker-Task Specialization Model for Crowdsourcing: Efficient Inference and Fundamental Limits](https://mlsanity.com/paper/2111.12550/) (2021)
- [Data Consistency for Weakly Supervised Learning](https://mlsanity.com/paper/2202.03987/) (2022)
- [Identify ambiguous tasks combining crowdsourced labels by weighting Areas Under the Margin](https://mlsanity.com/paper/2209.15380/) (2022)
- [Weakly Supervised Label Learning Flows](https://mlsanity.com/paper/2302.09649/) (2023)

## Abstract (arXiv)

There is a rapidly increasing interest in crowdsourcing for data labeling. By crowdsourcing, a large number of labels can be often quickly gathered at low cost. However, the labels provided by the crowdsourcing workers are usually not of high quality. In this paper, we propose a minimax conditional entropy principle to infer ground truth from noisy crowdsourced labels. Under this principle, we derive a unique probabilistic labeling model jointly parameterized by worker ability and item difficulty. We also propose an objective measurement principle, and show that our method is the only method which satisfies this objective measurement principle. We validate our method through a variety of real crowdsourcing datasets with binary, multiclass or ordinal labels.