# Spectral Clustering for Crowdsourcing with Inherently Distinct Task Types

arXiv: https://arxiv.org/abs/2302.07393 · HTML: https://mlsanity.com/paper/2302.07393/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 14 Feb 2023.

## Builds on (most cited first, 8 of 8)

- [Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing](https://mlsanity.com/paper/1406.3824/) (2014)
- [Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues](https://mlsanity.com/paper/1510.05610/) (2015)
- [A Permutation-based Model for Crowd Labeling: Optimal Estimation and Robustness](https://mlsanity.com/paper/1606.09632/) (2016)
- [Consistency of weighted majority votes](https://mlsanity.com/paper/1312.0451/) (2013)
- [Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers](https://mlsanity.com/paper/1904.11608/) (2019)
- [Exact Exponent in Optimal Rates for Crowdsourcing](https://mlsanity.com/paper/1605.07696/) (2016)
- [Achieving Budget-optimality with Adaptive Schemes in Crowdsourcing](https://mlsanity.com/paper/1602.03481/) (2016)
- [Reducing Crowdsourcing to Graphon Estimation, Statistically](https://mlsanity.com/paper/1703.08085/) (2017)

## Abstract (arXiv)

The Dawid-Skene model is the most widely assumed model in the analysis of crowdsourcing algorithms that estimate ground-truth labels from noisy worker responses. In this work, we are motivated by crowdsourcing applications where workers have distinct skill sets and their accuracy additionally depends on a task's type. While weighted majority vote (WMV) with a single weight vector for each worker achieves the optimal label estimation error in the Dawid-Skene model, we show that different weights for different types are necessary for a multi-type model. Focusing on the case where there are two types of tasks, we propose a spectral method to partition tasks into two groups that cluster tasks by type. Our analysis reveals that task types can be perfectly recovered if the number of workers n scales logarithmically with the number of tasks d. Any algorithm designed for the Dawid-Skene model can then be applied independently to each type to infer the labels. Numerical experiments show how clustering tasks by type before estimating ground-truth labels enhances the performance of crowdsourcing algorithms in practical applications.