Regression is a fundamental task in machine learning that has garnered
extensive attention over the past decades. The conventional approach for
regression involves employing loss functions that primarily concentrate on
aligning model prediction with the ground truth for each individual data
sample, which, as we show, can result in sub-optimal prediction of the
relationships between the different samples. Recent research endeavors have
introduced novel perspectives by incorporating label similarity information to
regression. However, a notable gap persists in these approaches when it comes
to fully capturing the intricacies of the underlying ground truth function. In
this work, we propose FAR (Function Aligned Regression) as a arguably better
and more efficient solution to fit the underlying function of ground truth by
capturing functional derivatives. We demonstrate the effectiveness of the
proposed method practically on 2 synthetic datasets and on 8 extensive
real-world tasks from 6 benchmark datasets with other 8 competitive baselines.
The code is open-sourced at https://github.com/DixianZhu/FAR.

通过函数导数的方法，我们提出了 FAR（函数对齐回归）作为一种更好更高效的解决方案，以适应底层真实函数，并在 2 个合成数据集和 6 个基准数据集的 8 个广泛真实世界任务中，与其他 8 个竞争基线进行实证展示。