[matrix2596]

I'm building upon insights from this paper (https://arxiv.org/pdf/2403.03950.pdf) and believe that classification can sometimes outperform regression, even when dealing with continuous output values. This is particularly true in scenarios where the output is noisy and may assume various values (multi modal). By treating the problem as classification over discrete bins, we can obtain an approximate distribution over these bins, rather than settling for a single, averaged value as regression would yield. This approach not only facilitates sampling but may also lead to more favorable loss landscapes. The linked paper in this comment provides more details of this idea.

[lamename]

Isn't it a given that classification would "outperform" regression, assuming n_classes < n_possible_continuous_labels? Turning a regression problem into a classification problem bins the data, offers more examples per label, simplifying the problem, with a tradeoff in what granularity you can predict.

(It depends on what you mean by "outperform" since metrics for classification and regression aren't always comparable, but I think I'm following the meaning of your comment overall)