Tabular Classification

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2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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From Prompts to Trees: Effective LLM-Guided Tree Generation for Few-Shot Tabular Classification
Oct 7, 2026Yue Qiu, Zekang Du, Yiqun Diao +2Tabular Classification Decision Tree Learning
School of Computer Science and Technology, Huazhong University of Science and Technology · School of Computing, National University of Singapore
Efficient Provably Private Classification with a Tabular Foundation Model
Oct 7, 2026Talal Alrawajfeh, Cristiana Diaconu, Ossi Räisä +5Privacy-Preserving ML Tabular Foundation Models
Department of Computer Science, University of Helsinki, Helsinki, Finland · Department of Engineering, University of Cambridge, Cambridge, United Kingdom · CISPA Helmholtz Center for Information Security, Saarbrücken, Germany +1
Distilling Foundation Models for Agentic What-If Reasoning:Cost, Latency, and Governance in a Hybrid LLM+SLM Architecture
Sep 14, 2026Sourish Dey, Aditya KumarModel Compression Tabular Foundation Models
Machine Learning SumUp, Berlin
TEFM: Token-Efficient Faithful Modeling for Structured Data
Sep 9, 2026Zhichao Hou, Lingdao Sha, Xueyu Mao +3LLM Interpretability Tabular Classification
North Carolina State University · Amazon Web Services
Why Large Language Models Fail at Tabular Prediction
Aug 3, 2026Marta Garnelo, Wojciech M. CzarneckiLLM Evaluation Tabular Classification
Fundamental Technologies
Understanding Context Sampling in TabPFN on Small Tabular Datasets
Jul 29, 2026Mohammed AbdullahTabular Foundation Models Tabular Classification
Department of AI & Data Science Anna University
Is the Statistical Advantage Worth the Cost? An Empirical Comparison of KANs and MLPs for Structured Data Classification
Jul 15, 2026Matthew Steven P. Toledo, Justine Raphael H. Jacinto, Vivekjeet Singh Chambal +3Multilayer Perceptrons Tabular Classification
Institute of Computer Science, University of the Philippines Los Baños, Philippines · Machine Learning and Artificial Intelligence Applications Lab, University of the Philippines Los Baños, Philippines
Pattern-Aware Graph Neural Networks for Handling Missing Data
Jul 9, 2026Minett Tran, Taehee JeongGraph Neural Networks Learning with Missing Data
San Jose State University
Complexity-Budgeted, Interaction-Aware Interpretable Model for Tabular Data
Jul 8, 2026Srikumar KrishnamoorthyFeature Interaction Modeling Interpretable ML
Information Systems Area, Indian Institute of Management Ahmedabad, India
Choosing a parallel heterogeneous ensemble method for tabular classification
Jul 6, 2026Vassili Maillet, Gustavo, Angulo +1Ensemble Learning Tabular Classification
Jesus · Mines Paris, PSL University, France
Hyperbolic Manifold Constrained Tabular Neural Network
Jun 23, 2026Tian Li, Huizhi Liang, Lucy Robinson +1Tabular Classification Tabular Representation Learning
Newcastle University Newcastle upon Tyne, UK
Evaluating Local Explainability Metrics for Machine Learning Models on Tabular Data
May 26, 2026Tomás Pereira, João Vitorino, Eva Maia +1Feature Attribution Explainable Artificial Intelligence
GECAD, ISEP, Polytechnic of Porto, Porto, Portugal
LLMTabBench: Evaluating LLMs on Binary Tabular Classification From Zero to Few Shots
May 23, 2026Daria Grushina, Kseniia Kuvshinova, Alina Kostromina +3Zero-Shot Learning LLM Evaluation
Sb AI Lab, HSE University, NES · NES · Russia +4
Correcting Class Imbalance in Prior-Data Fitted Networks for Tabular Classification
May 20, 2026Samuel McDowell, Nathan Stromberg, Lalitha SankarClass-Imbalanced Learning Tabular Classification
School of Electrical, Computer and Energy Engineering · Arizona State University, Tempe, AZ
Ensembling Tabular Foundation Models - A Diversity Ceiling And A Calibration Trap
May 18, 2026Aditya Tanna, Yash Desai, Pratinav Seth +3Tabular Foundation Models Ensemble Learning
Lexsi Labs
TabH2O: A Unified Foundation Model for Tabular Prediction
May 18, 2026Pascal Pfeiffer, Dmitry Gordeev, Mathias Müller +6Tabular Foundation Models Tabular Classification
TILBench: A Systematic Benchmark for Tabular Imbalanced Learning Across Data Regimes
May 14, 2026Ruizhe Liu, Jiaqi LuoClass-Imbalanced Learning Tabular Classification
School of Mathematical Sciences, Soochow University, No.1 Shizi Street, Suzhou, 215006, Jiangsu Province, China
MaskTab: Scalable Masked Tabular Pretraining with Scaling Laws and Distillation for Industrial Classification
May 12, 2026Bo Zheng, Yudong Chen, Zihua Xiong +4Tabular Foundation Models Self-Supervised Pre-Training
Zhejiang University · MyBank, Ant Group
BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification
May 7, 2026Yi-Siang Wang, Kuan-Yu Chen, Yu-Chen Den +1Ensemble Learning LLM Fine-Tuning
SinoPac Holdings

Oct 7, 2026, cs.LGJ/K move · Enter open · S save

From Prompts to Trees: Effective LLM-Guided Tree Generation for Few-Shot Tabular Classification

Yue Qiu, Zekang Du, Yiqun Diao, Bingsheng He+1

School of Computer Science and Technology, Huazhong University of Science and Technology · School of Computing, National University of Singapore

Tabular Classification Decision Tree Learning LLM Prompting Knowledge Distillation

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While Large Language Models (LLMs) possess rich world knowledge and impressive generalization capabilities, their direct application to tabular data classification is hindered by high inference costs and limited interpretability. In contrast, decision trees are fast and transparent but often underperform in low-data regimes. In this work, we propose a novel framework that bridges these paradigms by distilling LLM knowledge into interpretable decision trees under a few-shot learning setting. Instead of directly prompting the LLM to generate full trees, which is often unstable and inefficient, we develop a three-stage paradigm that prompts the LLM to generate rules and organize the rules into a tree. Experiments on multiple real-world tabular datasets demonstrate that our method achieves superior accuracy and interpretability with significantly lower prompting overhead compared to existing baselines.

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