解决表格数据分布偏移问题，提升模型鲁棒性与泛化能力
Distribution Shift Aware Neural Tabular Learning
- 将表格学习转为可微的连续表示生成，支持端到端优化
- 在多种真实分布偏移下，性能显著优于现有方法
- 适合实际场景中数据分布变化大的表格学习任务
表格学习将原始特征映射到优化空间以服务下游任务，但在训练与测试数据存在分布偏移时性能下降。本文将此问题形式化为分布偏移表格学习（DSTL），提出一种感知偏移的特征变换（SAFT）框架。SAFT将表格学习从离散搜索转化为连续表示生成范式，实现对变换后特征集的可微优化。通过三种机制保障鲁棒性：(i) 基于嵌入去相关和样本重加权的抗偏移表示；(ii) 通过次优嵌入平均实现平坦性感知生成；(iii) 基于归一化的训练与测试分布对齐。大量实验表明，SAFT在多种真实世界分布偏移下，持续优于现有表格学习方法，在鲁棒性、有效性与泛化能力上表现更佳。
原文摘要 · Abstract (English)
Tabular learning transforms raw features into optimized spaces for downstream tasks, but its effectiveness deteriorates under distribution shifts between training and testing data. We formalize this challenge as the Distribution Shift Tabular Learning (DSTL) problem and propose a novel Shift-Aware Feature Transformation (SAFT) framework to address it. SAFT reframes tabular learning from a discrete search task into a continuous representation-generation paradigm, enabling differentiable optimization over transformed feature sets. SAFT integrates three mechanisms to ensure robustness: (i) shift-resistant representation via embedding decorrelation and sample reweighting, (ii) flatness-aware generation through suboptimal embedding averaging, and (iii) normalization-based alignment between training and test distributions. Extensive experiments show that SAFT consistently outperforms prior tabular learning methods in terms of robustness, effectiveness, and generalization ability under diverse real-world distribution shifts.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。