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.

翻译：回归方法通常假设训练时可获得准确标签。然而在某些场景中，获取准确标签并不可行，此时需依赖持有不同意见的多位专家。现有处理含噪标签的方法往往对回归函数施加了限制性假设。与此相反，本文提出了一种更具灵活性的新方法。该方法包含两个步骤：首先估计每位标注者的专业水平，然后利用学习到的权重整合专家意见。随后，我们将加权平均值对输入特征进行回归，以构建预测模型。所提方法在理论上得到严格论证，并在模拟数据和真实数据上的实验结果表明其性能优于现有技术。此外，该方法的灵活性允许在两个步骤中任意使用机器学习技术。总之，该方法为训练基于多元专家意见的含噪标签回归模型提供了一种简单、快速且有效的解决方案。