@inproceedings{zheng-etal-2026-masktab,
title = "{M}ask{T}ab: Scalable Masked Tabular Pretraining with Scaling Laws and Distillation for Industrial Classification",
author = "Zheng, Bo and
Chen, Yudong and
Xiong, Zihua and
Fang, Shuai and
He, Peidong and
Yang, Yang and
Guo, Sheng",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.2053/",
pages = "41268--41280",
ISBN = "979-8-89176-395-1",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T MaskTab: Scalable Masked Tabular Pretraining with Scaling Laws and Distillation for Industrial Classification
%A Zheng, Bo
%A Chen, Yudong
%A Xiong, Zihua
%A Fang, Shuai
%A He, Peidong
%A Yang, Yang
%A Guo, Sheng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zheng-etal-2026-masktab
%X 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.
%U https://aclanthology.org/2026.findings-acl.2053/
%P 41268-41280
Markdown (Informal)
[MaskTab: Scalable Masked Tabular Pretraining with Scaling Laws and Distillation for Industrial Classification](https://aclanthology.org/2026.findings-acl.2053/) (Zheng et al., Findings 2026)
ACL
- Bo Zheng, Yudong Chen, Zihua Xiong, Shuai Fang, Peidong He, Yang Yang, and Sheng Guo. 2026. MaskTab: Scalable Masked Tabular Pretraining with Scaling Laws and Distillation for Industrial Classification. In Findings of the Association for Computational Linguistics: ACL 2026, pages 41268–41280, San Diego, California, United States. Association for Computational Linguistics.