# The Majority Vote Paradigm Shift: When Popular Meets Optimal

arXiv: https://arxiv.org/abs/2502.12581 · HTML: https://mlsanity.com/paper/2502.12581/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 52 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Builds on (most cited first, 14 of 14)

- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [Are Anchor Points Really Indispensable in Label-Noise Learning?](https://mlsanity.com/paper/1906.00189/) (2019)
- [Simple, Robust and Optimal Ranking from Pairwise Comparisons](https://mlsanity.com/paper/1512.08949/) (2015)
- [Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing](https://mlsanity.com/paper/1406.3824/) (2014)
- [Second Order PAC-Bayesian Bounds for the Weighted Majority Vote](https://mlsanity.com/paper/2007.13532/) (2020)
- [Identifiability of Label Noise Transition Matrix](https://mlsanity.com/paper/2202.02016/) (2022)
- [To Aggregate or Not? Learning with Separate Noisy Labels](https://mlsanity.com/paper/2206.07181/) (2022)
- [Error Rate Bounds and Iterative Weighted Majority Voting for Crowdsourcing](https://mlsanity.com/paper/1411.4086/) (2014)
- [Incorporating Worker Perspectives into MTurk Annotation Practices for NLP](https://mlsanity.com/paper/2311.02802/) (2023)
- [A Permutation-based Model for Crowd Labeling: Optimal Estimation and Robustness](https://mlsanity.com/paper/1606.09632/) (2016)
- [Truth Inference at Scale: A Bayesian Model for Adjudicating Highly Redundant Crowd Annotations](https://mlsanity.com/paper/1902.08918/) (2019)
- [Sample Prior Guided Robust Model Learning to Suppress Noisy Labels](https://mlsanity.com/paper/2112.01197/) (2021)
- [Efficient PAC Learning from the Crowd](https://mlsanity.com/paper/1703.07432/) (2017)
- [Learning from Crowds with Sparse and Imbalanced Annotations](https://mlsanity.com/paper/2107.05039/) (2021)

## Abstract (arXiv)

Reliably labelling data typically requires annotations from multiple human workers. However, humans are far from being perfect. Hence, it is a common practice to aggregate labels gathered from multiple annotators to make a more confident estimate of the true label. Among many aggregation methods, the simple and well known Majority Vote (MV) selects the class label polling the highest number of votes. However, despite its importance, the optimality of MV's label aggregation has not been extensively studied. We address this gap in our work by characterising the conditions under which MV achieves the theoretically optimal lower bound on label estimation error. Our results capture the tolerable limits on annotation noise under which MV can optimally recover labels for a given class distribution. This certificate of optimality provides a more principled approach to model selection for label aggregation as an alternative to otherwise inefficient practices that sometimes include higher experts, gold labels, etc., that are all marred by the same human uncertainty despite huge time and monetary costs. Experiments on both synthetic and real world data corroborate our theoretical findings.