Tabular Prediction

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Tabular prediction studies how to learn from rows of examples and columns of mixed numerical, categorical, and sometimes text or relational features. Common approaches include gradient-boosted trees, feature engineering, neural networks, and tabular foundation models pretrained on synthetic data for in-context learning; graph methods can add relationships across rows or tables. Benchmarks compare these methods on classification and regression, while applied work also examines calibration, fairness, interpretability, and deployment costs. No model family dominates across settings: foundation models are often strong on small or in-distribution datasets, while large, high-dimensional, or shifted data still challenge them, making generalization and fair evaluation central open issues.

Latest papers

112 papers12 papers in the last four weeks, down 29% on the four weeks before. 0.1% of all new papers.

Auditing LLMs for Algorithmic Fairness in Casenote-Augmented Tabular Prediction
Apr 21, 2026Xiao Qi Lee, Ezinne Nwankwo, Angela ZhouLLM Auditing Algorithmic Fairness
Department of Computer Science San Jose State University · Department of Computer Science University of California, Berkeley · Department of Data Science and Operations University of Southern California
STRIKE: Additive Feature-Group-Aware Stacking Framework for Credit Default Prediction
Apr 19, 2026Swattik Maiti, Ritik Pratap Singh, Fardina Fathmiul AlamEnsemble Learning Tabular Prediction
Science Academy, University of Maryland, College Park MD, USA · Department of Computer Science, University of Maryland, College Park MD, USA
ReSS: Learning Reasoning Models for Tabular Data Prediction via Symbolic Scaffold
Apr 15, 2026Chenlang Yi, Gang Li, Zizhan Xiong +4Tabular Prediction Neuro-Symbolic Reasoning
Department of Computer Science & Engineering, Texas A&M University · Department of Computer Science, University of Florida
Integrating SAINT with Tree-Based Models: A Case Study in Employee Attrition Prediction
Apr 11, 2026Adil Derrazi, Javad Pourmostafa Roshan SharamiTabular Prediction Tabular ML
CSAI Department, Tilburg University, The Netherlands
OmniTabBench: Mapping the Empirical Frontiers of GBDTs, Neural Networks, and Foundation Models for Tabular Data at Scale
Apr 8, 2026Dihong Jiang, Ruoqi Cao, Zhiyuan Dang +8Tabular ML Tabular Prediction
Huawei Cloud · School of Intelligence Science and Technology, Peking University
Toward Reducing Unproductive Container Moves: Predicting Service Requirements and Dwell Times
Apr 6, 2026Elena Villalobos, Adolfo De Unánue T., Fernanda Sobrino +4Supply Chain Management Tabular Prediction
Tecnológico de Monterrey, Mexico City, Mexico · Centro de Ciencia de Datos e Inteligencia Artificial School of Government and Public Transformation Tecnológico de Monterrey Mexico City, Mexico · Container Terminal Operations, Veracruz, Mexico +1
Vision-based Deep Learning Analysis of Unordered Biomedical Tabular Datasets via Optimal Spatial Cartography
Mar 24, 2026Sakib Mostafa, Tarik Massoud, Maximilian Diehn +2Tabular Prediction Tabular Representation Learning
Department of Radiation Oncology, Stanford University, Stanford, California, USA · Department of Radiology, Stanford University, Stanford, California, USA · Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA +1
Embedding World Knowledge into Tabular Models: Towards Best Practices for Embedding Pipeline Design
Mar 18, 2026Oksana Kolomenko, Ricardo Knauer, Erik RodnerTabular Prediction Tabular Representation Learning
KI-Werkstatt, University of Applied Sciences Berlin HTW Berlin, 10313 Berlin, Germany
Deep Tabular Representation Corrector
Mar 17, 2026Hangting Ye, Peng Wang, Wei Fan +4Tabular ML Tabular Representation Learning
School of Artificial Intelligence, Jilin University, China. · School of Computer Science, University of Auckland, New Zealand. · Chinese University of Hong Kong, Shenzhen, China. +3
HAPEns: Hardware-Aware Post-Hoc Ensembling for Tabular Data
Mar 11, 2026Jannis Maier, Lennart PuruckerEnsemble Learning Tabular Prediction
Helmholtz-Zentrum Berlin · TU Dortmund · Prior Labs +1
The Value of Graph-based Encoding in NBA Salary Prediction
Mar 5, 2026Junhao Su, David Grimsman, Christopher ArchibaldSports Analytics Tabular Prediction
Department of Computer Science Brigham Young University Provo, UT, USA
Machine Learning Grade Prediction Using Students' Grades and Demographics
Feb 28, 2026Mwayi Sonkhanani, Symon Chibaya, Clement N. NyirendaEducational Data Mining Educational Assessment
Malawi University of Science and Technology, P.O.Box 5196, Limbe, Malawi
Closing the gap on tabular data with Fourier and Implicit Categorical Features
Feb 26, 2026Marius Dragoi, Florin Gogianu, Elena BurceanuTabular Prediction Tabular Representation Learning
Bitdefender, Romania
KMLP: A Scalable Hybrid Architecture for Web-Scale Tabular Data Modeling
Feb 26, 2026Mingming Zhang, Pengfei Shi, Zhiqing Xiao +8Tabular ML Tabular Prediction
Zhejiang University Hangzhou, China · Ant Group Hangzhou, China
Evaluating SAP RPT-1 for Enterprise Business Process Prediction: In-Context Learning vs. Traditional Machine Learning on Structured SAP Data
Feb 22, 2026Amit LalTabular Foundation Models Tabular Prediction
Microsoft Corporation
Machine Learning Based Prediction of Surgical Outcomes in Chronic Rhinosinusitis from Clinical Data
Feb 19, 2026Sayeed Shafayet Chowdhury, Karen D'Souza, V. Siva Kakumani +9Clinical Outcome Prediction Clinical Decision Support
Purdue University, Indianapolis, IN, USA · Indiana University Indianapolis, Indianapolis, IN, USA · Idaho National Laboratory, Idaho Falls, ID, USA +5
tensorFM: Low-Rank Approximations of Cross-Order Feature Interactions
Feb 16, 2026Alessio Mazzetto, Mohammad Mahdi Khalili, Laura Fee Nern +3Feature Interaction Modeling Tabular Prediction
Brown University · Department of Computer Science Brown University Providence, RI, United States · Ohio State University +9
TabSieve: Explicit In-Table Evidence Selection for Tabular Prediction
Feb 12, 2026Yongyao Wang, Ziqi Miao, Lu Yang +4In-Context Example Selection Reinforcement Learning
Gaoling School of Artificial Intelligence, Renmin University of China · Shanghai AI Laboratory
TabICLv2: A better, faster, scalable, and open tabular foundation model
Feb 11, 2026Jingang Qu, David Holzmüller, Gaël Varoquaux +1Synthetic Data Pretraining Tabular Foundation Models
SODA Team, INRIA Saclay, Palaiseau, France · Probabl, France
MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Tabular Prediction
Feb 10, 2026Zizheng Zhang, Yiming Li, Justin Xu +6Interpretable ML Clinical Prediction
Microsoft Research · University of Oxford
Grables: When Graph Structure Helps Tabular Prediction
Feb 3, 2026Tamara Cucumides, Floris GeertsTabular ML Tabular Prediction
Department of Computer Science, University of Antwerp. Belgium.
Strengthening LLMs for Tabular Prediction with Structural Priors
Oct 20, 2025Pengxiang Cai, Zihao Gao, Wanchen Lian +2Tabular Foundation Models RL for Language Models
The Hong Kong University of Science and Technology (Guangzhou) · Zhejiang University

Apr 21, 2026J/K move · Enter open · S save

Auditing LLMs for Algorithmic Fairness in Casenote-Augmented Tabular Prediction

Xiao Qi Lee, Ezinne Nwankwo, Angela Zhou

Department of Computer Science San Jose State University · Department of Computer Science University of California, Berkeley · Department of Data Science and Operations University of Southern California

LLM Auditing Algorithmic Fairness Tabular Prediction

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LLMs are increasingly being considered for prediction tasks in high-stakes social service settings, but their algorithmic fairness properties in this context are poorly understood. In this short technical report, we audit the algorithmic fairness of LLM-based tabular classification on a real housing placement prediction task, augmented with street outreach casenotes from a nonprofit partner. We audit multi-class classification error disparities. We find that a fine-tuned model augmented with casenote summaries can improve accuracy while reducing algorithmic fairness disparities. We experiment with variable importance improvements to zero-shot tabular classification and find mixed results on resulting algorithmic fairness. Overall, given historical inequities in housing placement, it is crucial to audit LLM use. We find that leveraging LLMs to augment tabular classification with casenote summaries can safely leverage additional text information at low implementation burden. The outreach casenotes are fairly short and heavily redacted. Our assessment is that LLM zero-shot classification does not introduce additional textual biases beyond algorithmic biases in tabular classification. Combining fine-tuning and leveraging casenote summaries can improve accuracy and algorithmic fairness.