Tabular Prediction

Follow this topic Ask about this topic

Momentum

8 papers in the last four weeks, down 11% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 73

All topics

A Fair Benchmarking of Deep Relational Database Learning Models
Jul 4, 2026Kazi F. Akhter, Bharath Ajendla, Manar D. SamadTransformer Graph Representation Learning
Department of Computer Science Tennessee State University Nashville, TN, USA · SAP AI Research and Innovation SAP Labs Palo Alto, CA, USA
Exploring Differences Between Tabular Enterprise Data and Public Benchmarks
Jun 29, 2026Myung Jun Kim, Maximilian Schambach, Frank Essenberger +2Benchmark Design Tabular ML
SAP SE, Germany.
Beyond IID: How General Are Tabular Foundation Models, Really?
Jun 29, 2026Lennart Purucker, Andrej Tschalzev, Nick Erickson +7Tabular Foundation Models OOD Generalization
1Prior Labs · University of Mannheim · 4INRIA Saclay +5
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
Predicting High-Risk Colorectal Polyps in African Americans Using Pre-Colonoscopy Clinical Features: Machine Learning Model Development and Temporal Validation
Jun 19, 2026Basheer Qolomany, Mrinalini Deverapall, Adeyinka Laiyemo +5Tabular Prediction Clinical Risk Prediction
Departments of Internal Medicine, Pathology, and Biochemistry, and Cancer Center, Howard University College of Medicine, Washington, D.C., 20059, USA. · Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway. · Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London Hospital, London, UK.
LLMs on Tabular Data with Limited Semantics: Evidence from Industrial Car Retrofit Prediction
Jun 13, 2026Aina Vila Pons, Ioannis Tzachristas, Constantinos AntoniouTabular Prediction Tabular Regression
Chair of Transportation Systems Engineering, Technical University of Munich, Germany · BMW Group, Munich, Germany
Leveraging Physiological Signals to Predict Exam Outcomes with Machine Learning
Jun 12, 2026Lala Yamazaki, Ramchandra RimalEducational Assessment AI in Education
Department of Mathematical Sciences, Middle Tennessee State University, 1301 E Main St, Murfreesboro, 37132, TN, USA.
Probabilistic Salary Prediction with Graph Attention Networks and a Mixture Density Network
Jun 10, 2026Zhipei Qin, Mohammad Shokri, N. van Weeren +1Tabular Prediction
Leiden Institute of Advanced Computer Science (LIACS) · Randstad N.V.
TRL-Bench: Standardizing Cross-Paradigm Representation-Level Evaluation of Tabular Encoders
Jun 8, 2026Wei Pang, Xiangru Jian, Hehan Li +10Representation Learning Tabular Representation Learning
The Chinese University of Hong Kong, Shenzhen · University of Waterloo · The University of Hong Kong +2
TLRD: Teaching LLMs to Reason over Tabular Data with Tri-Level Rationale Distillation
Jun 6, 2026Tianyuan Liang, Xuwei Tan, Lei Shi +6LLM Grounding Knowledge Distillation
The Ohio State University · Stevens Institute of Technology
TabSwift: An Efficient Tabular Foundation Model with Row-Wise Attention
Jun 5, 2026Si-Yang Liu, Han-Jia YeTabular Foundation Models Efficient Inference
School of Artificial Intelligence, Nanjing University, China · National Key Laboratory for Novel Software Technology, Nanjing University, China.
GOTabPFN: From Feature Ordering to Compact Tokenization for Tabular Foundation Models on High-Dimensional Data
Jun 3, 2026Al Zadid Sultan Bin Habib, Md Younus Ahamed, Prashnna Kumar Gyawali +2Tabular Foundation Models Tabular Prediction
Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA · Scientific Computing and Imaging Institute & Department of Biomedical Informatics, The University of Utah, Salt Lake City, UT 84112, USA
Towards Pretraining Text Encoders for TabPFN
Jun 3, 2026Mustafa Tajjar, Alexander Pfefferle, Lennart Purucker +1Tabular Foundation Models Fine-Tuning
Department of Computer Science, University of Freiburg, Freiburg, Germany · ELLIS Institute Tübingen, Tübingen, Germany · Prior Labs, Freiburg, Germany.
Speedrunning Tabular Foundation Model Pretraining
Jun 2, 2026Salih Bora Ozturk, Alexander Pfefferle, Frank HutterTabular Foundation Models Tabular Prediction
University of Freiburg, Freiburg im Breisgau, Germany · ELLIS Institute Tübingen, Tübingen, Germany · Prior Labs, Freiburg im Breisgau, Germany
TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
Jun 1, 2026Andrej Tschalzev, Nick Erickson, Yuyang Wang +4Tabular ML Automated Feature Engineering
University of Mannheim · Prior Labs · AWS +3
ChurnNet: A Optimized Modern AI for Churn Prediction
May 29, 2026Syed Saad Saif, Giulio Maggiore, Paolo Russo +1Time Series Classification Multi-Task Learning
Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, Italy · Department of Law and Economics, UnitelmaSapienza University of Rome, Rome, Italy · R&D Center, Token Financial Technologies, Istanbul, Turkey +1
FlagGAM: Rule-Basis Generalized Additive Models for Explainable Tabular Prediction
May 29, 2026Zijie Zhao, Roy E. WelschInterpretable ML Corruption Robustness
EECS Department, Massachusetts Institute of Technology, Cambridge, MA, USA · Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA
Evolutionary Rule Extraction from Corporate Default Prediction Models
May 28, 2026Desirè Fabbretti, Matteo Pasquino, Elia Pacioni +2Interpretable ML Credit Scoring
Department of Management, Università Politecnica delle Marche, Ancona, Italy · HES-SO Valais-Wallis, 3960 Sierre, Switzerland · University of Extremadura, 06800 Mérida, Spain
The Good, the Bad, and the Ugly of Markov Boundary for Tabular Prediction
May 28, 2026Shu Wan, Abhinav Gorantla, Huan Liu +1Structural Causal Models Causal Discovery
Arizona State University Tempe, United States
Explaining Tabular Foundation Model Differences Through Meta-Features
May 27, 2026Markus Herre, Andrej Tschalzev, Sascha Marton +1Tabular Foundation Models Tabular ML
Clausthal University of Technology, Clausthal-Zellerfeld, Germany · University of Mannheim, Mannheim, Germany
PINE: Pruning Boosted Tree Ensembles with Conformal In-Distribution Prediction Equivalence
May 27, 2026Haruki Yajima, Yusuke MatsuiModel Compression Ensemble Learning
The University of Tokyo.
Customer Churn Prediction on Structured Data Using FT-Transformer and Stacking Ensembles
May 26, 2026Joyjit Roy, Samaresh Kumar Singh, Laxmi ShawTransformer Ensemble Learning
Independent Researcher, Austin, TX, USA · Independent Researcher, Leander, TX · Texas A & M University-Victoria, Victoria, TX
Analysis and Prediction of At-Risk Students Using Machine Learning Algorithms
May 24, 2026Soheila Gheisari, Hamid SalarianAI in Education Educational Data Mining
Department of Information Technology, Sydney International School of Technology and Commerce, Sydney, NSW 2000, Australia
Is TabPFN the Silver Bullet for Insurance Pricing?
May 21, 2026Bruno Deprez, Wouter Verbeke, Tim VerdonckTabular Foundation Models Tabular ICL
KU Leuven · University of Antwerp-imec
Foundation Models for Credit Risk Prediction: A Game Changer?
May 18, 2026Bart Baesens, Andreas Goethals, Stefan Lessmann +10Tabular Foundation Models AI Risk Management
Faculty of Economics and Business, KU Leuven, Belgium · School of Business and Economics, Humboldt University of Berlin, Germany · Department of Statistical and Actuarial Sciences, Western University, Canada +8
Avoiding Structural Failure Modes in Tabular Fair SSL: Online Primal-Dual Allocation under Confidence Gating
May 15, 2026Hangchuan Liang, Changchun LiPrimal-Dual Optimization Group Fairness
College of Computer Science and Technology, Jilin University, China · Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Jilin University, China
LoMETab: Beyond Rank-1 Ensembles for Tabular Deep Learning
May 14, 2026Changryeol Choi, Hyewon Park, Yujin Kwon +1Ensemble Learning Low-Rank Adaptation
CJ Logistics
TabPFN-3: Technical Report
May 13, 2026Léo Grinsztajn, Klemens Flöge, Oscar Key +38Tabular Foundation Models Efficient Neural Network Inference
GHGbench: A Unified Multi-Entity, Multi-Task Benchmark for Carbon Emission Prediction
May 13, 2026Yifan Duan, Siyuan Zheng, Lihuan Li +2Time Series Forecasting OOD Generalization
School of Computer Science and Engineering University of New South Wales
Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling
May 12, 2026Eilam Shapira, Moshe Tennenholtz, Roi ReichartNext-Action Prediction LLM Agents
Faculty of Data and Decision Sciences Technion – Israel Institute of Technology Haifa, Israel

Jul 4, 2026, cs.DBJ/K move · Enter open · S save

A Fair Benchmarking of Deep Relational Database Learning Models

Kazi F. Akhter, Bharath Ajendla, Manar D. Samad

Department of Computer Science Tennessee State University Nashville, TN, USA · SAP AI Research and Innovation SAP Labs Palo Alto, CA, USA

Transformer Graph Representation Learning Relational Deep Learning Tabular Prediction Relational Databases

Open paper AskPDF arXiv

Relational databases (RDBs) are the primary data infrastructure in many enterprises, yet recent deep learning methods designed for RDBs have been evaluated under inconsistent experimental protocols, making fair comparison difficult. We present one of the first systematic benchmarking studies of recently released deep learning methods for RDBs, evaluating them across five relational databases, with one classification and one regression task for each. We refactor all deep RDB models to allow the full range of experimental procedures to be applied consistently across all methods. Our findings indicate that the relational transformer (RT) approach delivers the strongest overall performance on both classification and regression tasks compared to the state-of-the-art graph-based modeling and learning of RDBs. Even for single-table learning tasks, deep learning methods designed for RDBs outperform the leading tabular foundation model, TabPFN 2.5. Extending learning from a single table (hop = 0) to multiple tables (hop = 1, 2) by connecting neighboring tables in relational databases enhances performance, but the additional benefit from higher hops diminishes as computational overhead grows. Deep RDB learning methods have the potential to challenge state-of-the-art tabular foundation models, especially on large-scale enterprise data. The source code for this benchmarking study is publicly available.

Momentum

Related topics

Latest papers 73