Title: Enhancing Deep Tabular Models with GBDT-Guided Piecewise-Linear Embeddings
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Tabular learning, Feature heterogeneity

Abstract: Tabular data remains central to many scientific and industrial applications. Recently, deep learning models are emerging as a powerful tool for tabular data prediction, outperforming traditional methods such as Gradient Boosted Decision Trees (GBDTs). Despite this success, the fundamental challenge of feature heterogeneity still remains. Unlike in image or text modalities where features are semantically homogeneous, each tabular feature often carries a distinct semantic meaning and distribution. A common strategy to address the heterogeneity is to project features into a shared high-dimensional vector space. Among the various feature types in tabular data, categorical features are effectively embedded via embedding bags, which assign a learnable vector to each unique category. In contrast, effective embeddings for numerical features remain underexplored. In this paper, we argue that piecewise-linear functions are well suited to modeling the irregular and high-frequency patterns often found in tabular data, provided that breakpoints are carefully chosen. To this end, we propose GBDT-Guided Piecewise-Linear (GGPL) embeddings, a method comprising breakpoints initialization using GBDT split thresholds, stable breakpoint optimization using reparameterization, and stochastic regularization via breakpoints deactivation. Thorough evaluation on 46 datasets shows that applying GGPL to a range of state-of-the-art tabular models consistently improves baseline models, demonstrating its effectiveness and versatility. The code is available in the supplementary material.