arXiv cs.AI· Danil Rudenko, Georgii Bulgakov, Nikolay Kartashev, Ivan Rubachev, Daniil Krasylnikov, Artem Babenko·· 6 天前AI 评分22
重新审视表格深度回归：目标函数与全局早停的局限
Revisiting Tabular Deep Regression: Objectives and the Limits of Global Early Stopping
AI 导读
一项系统性重评估发现，多数针对表格回归提出的目标函数相比调优后的 MSE 基线收益有限或不一致，而 quantile loss 在测试的多个神经骨干网络上取得一致的总体提升。
正文 · 原文
Abstract:While deep learning models for tabular data have recently received significant research attention, the choice of objective functions for regression tasks remains an understudied degree of freedom. Several lines of research offer alternatives to the standard Mean Squared Error (MSE), but differences in datasets, backbones, and evaluation protocols make their practical benefits difficult to compare. In this work, we address this gap by performing a systematic re-evaluation of regression objectives across a broad collection of established tabular datasets and multiple neural backbones. Our comparison reveals that many proposed objectives provide limited or inconsistent gains over a properly tuned MSE baseline, while quantile loss yields consistent aggregate improvements over MSE across the tested backbones. We then identify a limitation that remains even with this strong objective: regions with different levels of target noise can reach their best predictive performance at different training epochs. Consequently, a single global early-stopping checkpoint can impose a compromise between uncertainty regions, even when the objective can represent heteroskedastic target distributions. Motivated by this limitation, we introduce a heteroskedasticity-aware extension of quantile loss that uses input-dependent label smoothing as a potential remedy and improves over quantile loss on several backbones. Overall, our results provide a modern empirical picture of regression objectives for tabular deep learning and highlight how differences in optimal stopping times across uncertainty regions can limit the effectiveness of global early stopping. The code is available at this https URL.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.13909 [cs.LG] |
| (or arXiv:2610.13909v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.13909 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Nikolay Kartashev [view email]
[v1]
Fri, 9 Oct 2026 17:49:32 UTC (605 KB)
来源：arXiv cs.AI · arxiv.org