# Expertise-based Weighting for Regression Models with Noisy Labels

arXiv: https://arxiv.org/abs/2305.07430 · HTML: https://mlsanity.com/paper/2305.07430/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 12 May 2023.

## Builds on (most cited first, 6 of 6)

- [Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion](https://mlsanity.com/paper/1902.03680/) (2019)
- [Nonparametric Conditional Density Estimation in a High-Dimensional Regression Setting](https://mlsanity.com/paper/1604.00540/) (2016)
- [Learning Supervised Topic Models for Classification and Regression from Crowds](https://mlsanity.com/paper/1808.05902/) (2018)
- [Knowledge Learning with Crowdsourcing: A Brief Review and Systematic Perspective](https://mlsanity.com/paper/2206.09315/) (2022)
- [A Spectral Series Approach to High-Dimensional Nonparametric Regression](https://mlsanity.com/paper/1602.00355/) (2016)
- [Learning with many experts: model selection and sparsity](https://mlsanity.com/paper/1405.3292/) (2014)

## Abstract (arXiv)

Regression methods assume that accurate labels are available for training. However, in certain scenarios, obtaining accurate labels may not be feasible, and relying on multiple specialists with differing opinions becomes necessary. Existing approaches addressing noisy labels often impose restrictive assumptions on the regression function. In contrast, this paper presents a novel, more flexible approach. Our method consists of two steps: estimating each labeler's expertise and combining their opinions using learned weights. We then regress the weighted average against the input features to build the prediction model. The proposed method is formally justified and empirically demonstrated to outperform existing techniques on simulated and real data. Furthermore, its flexibility enables the utilization of any machine learning technique in both steps. In summary, this method offers a simple, fast, and effective solution for training regression models with noisy labels derived from diverse expert opinions.