  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.

Machine Learning;Machine Learning