MaskTab: Scalable Masked Tabular Pretraining with Scaling Laws and Distillation for Industrial Classification

ACL ARR 2026 January Submission9136 Authors

Keywords: Tabular foundation models, self-supervised pre-training, masked feature modeling

Abstract:

Tabular data forms the backbone of high-stakes decision systems in finance, healthcare, and beyond. Yet industrial tabular datasets are inherently difficult: high-dimensional, riddled with missing entries, and rarely labeled at scale. While foundation models have revolutionized vision and language, tabular learning still leans on handcrafted features and lacks a general self-supervised framework.
We present MaskTab, a unified pre-training framework designed specifically for industrial-scale tabular data. MaskTab encodes missing values via dedicated learnable tokens, enabling the model to distinguish structural absence from random dropout. It jointly optimizes a hybrid supervised pre-training scheme—utilizing a twin-path architecture to reconcile masked reconstruction with task-specific supervision—and an MoE-augmented loss that adaptively routes features through specialized subnetworks. On industrial-scale benchmarks, it achieves +5.04\% AUC and +8.28\% KS over prior art under rigorous scaling. Moreover, its representations distill effectively into lightweight models, yielding +2.55\% AUC and +4.85\% KS under strict latency and interpretability constraints, while improving robustness to distribution shifts. Our work demonstrates that tabular data admits a foundation-model treatment—when its structural idiosyncrasies are respected.

Paper Type: Long

Research Area: Financial Applications and Time Series

Research Area Keywords: Financial NLP,fraud detection,risk modeling

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 9136