Neural Regression For Scale-Varying Targets
This addresses a limitation in regression for settings with varying target scales, offering a solution to a known bottleneck in deep learning.
The paper tackles the problem of regression with scale-varying targets by proposing autoregressive regression, which learns a high-fidelity distribution through an autoregressive target decomposition, enabling effective handling of targets with different scales.
In this work, we demonstrate that a major limitation of regression using a mean-squared error loss is its sensitivity to the scale of its targets. This makes learning settings consisting of target's whose values take on varying scales challenging. A recently-proposed alternative loss function, known as histogram loss, avoids this issue. However, its computational cost grows linearly with the number of buckets in the histogram, which renders prediction with real-valued targets intractable. To address this issue, we propose a novel approach to training deep learning models on real-valued regression targets, autoregressive regression, which learns a high-fidelity distribution by utilizing an autoregressive target decomposition. We demonstrate that this training objective allows us to solve regression tasks involving targets with different scales.