Tabular Regression

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Xiaomi-TabLDM: A Tabular Foundation Model Technical Report
Sep 3, 2026Xiaomi-TabLDM Team, :, Penghui Wang +10Synthetic Data Pretraining Tabular Foundation Models
Xiaomi
Do Tabular Foundation Models Know Physics? Contamination, Units, and the Deterministic Limit
Sep 2, 2026Wassim Tenachi, Yashar Hezaveh, Laurence Perreault Levasseur +1Tabular Foundation Models Physical Reasoning
Département de physique, Université de Montréal · Mila – Quebec Artificial Intelligence Institute · Department of Computer Science and Operations Research, Université de Montréal[cs.LG]
Context Window Failures in Relational Foundation Models
Aug 31, 2026Denis Oliveira Correa, Francisco Galuppo AzevedoRelational Foundation Models Graph Foundation Models
Kunumi Institute, Brazil · Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
PATH: Next-Interval Prediction via Autoregressive Tree Hierarchy on Tabular Data
Aug 8, 2026Pengxiang Cai, Wanchen Lian, Chenyang Liu +4Regression Prediction Intervals Tabular Regression
The Hong Kong University of Science and Technology (Guangzhou) · Ant Group
Handling Missing Data in Probabilistic Regression Trees
Aug 6, 2026Taiane Schaedler Prass, Alisson Silva Neimaier, Guilherme PumiLearning with Missing Data Decision Tree Learning
a Graduate Program in Statistics - Universidade Federal do Rio Grande do Sul.
From field-scale to large-scale spectral libraries: Tabular foundation models in soil spectroscopy
Aug 1, 2026Viacheslav Barkov, Jonas Schmidinger, Robin Gebbers +1Tabular Foundation Models Recursive Least Squares
Osnabrück University, Joint Lab Artificial Intelligence and Data Science, Osnabrück, Germany · Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Department of Agromechatronics, Potsdam, Germany · German Research Center for Artificial Intelligence (DFKI), Cooperative and Autonomous Systems (CAS), Osnabrück, Germany
Conditioning Tree-Based Diffusions and Flows for Probabilistic Tabular Regression
Jul 30, 2026Silas KoemenFlow Matching Gradient-Boosted Decision Trees
Independent Researcher
Interpretable Fuzzy Rule-Based Regression Extension for Ex-Fuzzy Library
Jul 22, 2026Cayan Deniz Kucuktopana, Javier Fumanal-Idocin, Richard Pitts +1Interpretable ML Fuzzy Logic
School of Computer Science and Electronic Engineering University of Essex Colchester, United Kingdom
Accelerometry-Derived Digital Biomarkers for Cardiometabolic Risk: A Population-Representative Tabular Benchmark with Uncertainty Quantification
Jun 29, 2026Federico FelizziAlgorithmic Fairness Wearable Health Monitoring
SIIAM, Rome, Italy · enGene Statistics, Basel, Switzerland.
Counterfactual Residual Data Augmentation for Regression
Jun 26, 2026Hossein Mohebbi, Oliver Schulte, Ke Li +1Synthetic Tabular Data Generation Counterfactual Data Augmentation
University of Waterloo · Vector Institute · Simon Fraser University.
Solve for the Hyperparameter, Skip the Search: Kolmogorov-Optimal Scaling Laws for Spline Regression
Jun 22, 2026Yong Yi Bay, Kathleen A. YearickModel Selection Scaling Laws
PhD, University of Illinois at Urbana-Champaign
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
CLARITree: Cholesky and Lookahead Accelerations for Regression with Interpretable Piecewise Linear Trees
Jun 11, 2026Yixiao Wang, Hayden McTavish, Varun Babbar +2Tree Search Interpretable ML
Department of Computer Science, Duke University, Durham, USA · Department of Computer Science, University of British Columbia, Vancouver, Canada
RowNet: A Memory Transformer for Tabular Regression
Jun 3, 2026Askat Rakhymbekov, Gulshat MuhametjanovaMemory-Augmented Neural Networks Tabular Regression
Department of Applied Mathematics and Informatics Kyrgyz-Turkish Manas University Bishkek, Kyrgyzstan · Department of Applied Mathematics and Informatics2026 Kyrgyz-Turkish Manas University Bishkek, KyrgyzstanJun
On the Uncertainty Quantification Ability of Tabular Foundation Models
May 31, 2026Tyler R. Johnson, Kian Ben-Jacob, Nima Negarandeh +2Tabular Foundation Models Uncertainty Quantification
Department of Mechanical and Aerospace Engineering, University of California, Irvine · Department of Civil and Environmental Engineering, University of California, Irvine
Hinge Regression Trees and HRT-Boost: Newton-Optimized Oblique Learning for Compact Tabular Models
May 22, 2026Hongyi Li, Jun Xu, Hong YanGradient-Boosted Decision Trees Decision Tree Learning
School of Intelligence Science and Engineering, Harbin Institute of Technology, Shenzhen, 518055, China and with the Shenzhen Key Lab for Advanced Motion Control and Modern Automation Equipments, Shenzhen, 518055, China · Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong
Tabular foundation models for robust calibration of near-infrared chemical sensing data
May 20, 2026Robin Reiter, Denis Cornet, Fabien Michel +2Tabular Foundation Models Tabular Regression
1CIRAD, UMR AGAP Institut, F-34398 Montpellier, France · 2UMR AGAP Institut, Université de Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France · 3LIRMM, Université de Montpellier, CNRS, Montpellier, France
TabH2O: A Unified Foundation Model for Tabular Prediction
May 18, 2026Pascal Pfeiffer, Dmitry Gordeev, Mathias Müller +6Tabular Foundation Models Tabular Classification
Optimised Support Vector Regression for California Housing Price Prediction: The Critical Role of Feature Engineering and Hyperparameter Tuning
May 9, 2026Emmanuel AdutwumSupport Vector Machines Hyperparameter Optimization
Soka University of America, Aliso Viejo, California, United States.
Agentic-imodels: Evolving agentic interpretability tools via autoresearch
May 5, 2026Chandan Singh, Yan Shuo Tan, Weijia Xu +4Interpretable ML Automated Machine Learning
Microsoft Research · National University of Singapore
Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules
Mar 9, 2026Jonas Landsgesell, Pascal Knoll, Tizian WenzelTabular Foundation Models Proper Scoring Rules
University of Stuttgart (Stuttgart, Germany) · Ludwig Maximilian University of Munich (Munich, Germany) · Munich Center for Machine Learning (Munich, Germany)
Learning Interpretable Differentiable Logic Networks for Tabular Regression
May 29, 2025Chang Yue, Niraj K. JhaEfficient Neural Network Inference Neural Network Interpretability
Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA
Predicting Estimated Times of Restoration for Electrical Outages Using Longitudinal Tabular Transformers
May 1, 2025Bogireddy Sai Prasanna Teja, Valliappan Muthukaruppan, Carls BenjaminEvent Time Prediction Transformer
Exelon Corporation, Chicago, IL, USA
Ordinary Least Squares as an Attention Mechanism
Apr 13, 2025Philippe Goulet CoulombeTransformer Attention Linear Regression
Département des Sciences Économiques, Université du Québec à Montréal

Sep 3, 2026, cs.AIJ/K move · Enter open · S save

Xiaomi-TabLDM: A Tabular Foundation Model Technical Report

Xiaomi-TabLDM Team, :, Penghui Wang, Wei Liu+9

Xiaomi

Synthetic Data Pretraining Tabular Foundation Models Test-Time Scaling Tabular ICL Tabular Regression

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We introduce Xiaomi-TabLDM, a tabular large data foundation model for classification and regression via in-context learning, which delivers superior prediction accuracy without requiring task-specific fine-tuning. Pretrained exclusively on synthetic data generated from structural causal models (SCMs), our model enables more flexible context utilization and more efficient capacity scaling. i) A new performance standard. Strong regression performance across benchmarks: Xiaomi-TabLDM ranks 1st on OpenML-CTR23 and 2nd on regression across TALENT, TabArena, and BCCO, demonstrating consistently strong regression performance across four complementary benchmark suites. Favorable performance--efficiency trade-off: Xiaomi-TabLDM combines strong predictive performance with substantially lower computational cost. For example, on TabArena regression, it achieves the second-highest Elo while using 82% less training time and 68% less prediction time than the top-ranked TabFM. ii) Large-scale synthetic pretraining. Xiaomi-TabLDM expands the coverage and diversity of synthetic tabular data used for pretraining. We also adopt a three-stage training strategy together with dual-stream feature grouping, lightweight Attention Residual, and sparse Mixture-of-Experts, enabling Xiaomi-TabLDM to learn richer feature interactions and expert specialization across diverse tabular tasks. iii) Test-time scaling. Xiaomi-TabLDM further extends tabular prediction through test-time compute scaling, where allocating additional computation at inference time consistently improves predictive performance over the base model.

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Latest papers 24