KMLP用混合架构自动处理海量表格数据，省去人工调参。
KMLP: A Scalable Hybrid Architecture for Web-Scale Tabular Data Modeling
- 前段用可学习激活函数的KAN自动提取特征非线性变换
- 后端gMLP捕捉高阶特征交互，百亿级数据上性能领先
- 适合需要自动化、高扩展性的工业级表格建模场景
在包含数十亿样本和数百个异构数值特征的网络规模表格数据上进行预测建模面临显著可扩展性挑战。这些特征具有各向异性、重尾分布和非平稳性，导致梯度提升决策树等模型出现瓶颈，且需大量手动特征工程。我们提出KMLP，一种融合浅层科莫戈罗夫-阿诺德网络（KAN）前端与门控多层感知机（gMLP）主干的混合深度架构。KAN前端通过可学习激活函数自动建模每个特征的复杂非线性变换，gMLP主干则捕捉高阶特征交互。在公开基准和一个包含数十亿样本的工业数据集上的实验表明，KMLP达到当前最优性能，且在更大规模下对基线方法（如GBDTs）的优势更为明显，验证了其作为大规模网络表格数据可扩展深度学习范式的能力。
原文摘要 · Abstract (English)
Predictive modeling on web-scale tabular data with billions of instances and hundreds of heterogeneous numerical features faces significant scalability challenges. These features exhibit anisotropy, heavy-tailed distributions, and non-stationarity, creating bottlenecks for models like Gradient Boosting Decision Trees and requiring laborious manual feature engineering. We introduce KMLP, a hybrid deep architecture integrating a shallow Kolmogorov-Arnold Network (KAN) front-end with a Gated Multilayer Perceptron (gMLP) backbone. The KAN front-end uses learnable activation functions to automatically model complex non-linear transformations for each feature, while the gMLP backbone captures high-order interactions. Experiments on public benchmarks and an industrial dataset with billions of samples show KMLP achieves state-of-the-art performance, with advantages over baselines like GBDTs increasing at larger scales, validating KMLP as a scalable deep learning paradigm for large-scale web tabular data.
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