Tabular ML

ML: Machine Learning

Tabular machine learning studies prediction, discovery, and explanation from structured data with numerical, categorical, text, and missing values. Common approaches include gradient-boosted decision trees, neural networks with feature embeddings and attention, pretrained or in-context models, and automated feature engineering. Benchmarks compare these methods on classification, regression, anomaly detection, and specialized tasks, but no model family consistently leads across dataset sizes, feature types, and settings. A central challenge is making results generalize beyond clean IID benchmarks while balancing accuracy with robustness, interpretability, computational cost, and careful evaluation.

Latest papers

116 papers17 papers in the last four weeks, up 143% on the four weeks before. 0.1% of all new papers.

Geodesic-SVDD Latent Mixup for One-Shot Distributed Tabular Learning
Oct 24, 2026Andrea Moleri, Christian Internò, Markus Olhofer +2Federated Learning Tabular ML
Honda Research Institute Europe · University of Milan-Bicocca · Bielefeld University
When Do LLMs Help with Feature Selection? A Systematic Empirical Study
Oct 20, 2026Fateme Memar, Dongjie Wang, Arvin AgahFeature Selection Tabular ML
Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA · School of Information Science and Technology, Northeast Normal University, China
On Data Scaling in Tabular ML: More Data, Better Generalization?
Oct 19, 2026Ivan Rubachev, Nikita Lazarev, Artem BabenkoTabular ML Tabular Foundation Models
Yandex · HSE University
INCONVENIENT: A Benchmark to Assess LLM Generalization Capabilities from Convenience Samples
Oct 15, 2026Sara Rezaeimanesh, Chad Klochko, Kundan Thind +1Distribution Shift Robustness Tabular ML
Michigan State University, East Lansing, MI, USA · Henry Ford Health, Detroit, MI, USA
When Does Task-Aware Imputation Matter? An Analysis of Robustness Under Train-Test Mismatch
Oct 14, 2026Maria V. Salvador, Ana F. Sequeira, Jaime S. CardosoIncomplete Data Imputation Learning with Missing Data
INESC TEC University of Porto
Typed Decision Models on Tabular Data
Oct 14, 2026Baowen Zhang, Hangting Ye, Hongyuan ZhaTabular Prediction Tabular ML
University of Wisconsin–Madison · School of Artificial Intelligence, Jilin University · School of Data Science, Chinese University of Hong Kong, Shenzhen
SoK: Rethinking Membership Inference in Tabular Machine Learning
Oct 13, 2026Cristina Pêra, Tânia Carvalho, Maxime Cordy +1Membership Inference Attacks Tabular ML
TekPrivacy, Portugal · SnT, University of Luxembourg, Luxembourg · Department of Computer Science, Faculty of Sciences of the University of Porto, Portugal
TILE: Bridging LLMs and Table Foundation Models for Tabular Learning with Strings
Oct 12, 2026Félix Lefebvre, David Holzmüller, Gioia Blayer +3Tabular ML Tabular Representation Learning
SODA Team, Inria Saclay · SAP SE · Probabl
SelectArena: A Modern Benchmark for Feature Selection on Real-world Tabular Data
Oct 9, 2026Bastian Schäfer, Dominika Matus, Lennart Purucker +5Feature Selection Benchmark Design
Université du Littoral-Côte-d’Opale · University of Freiburg · Prior Labs +2
Compile the Table: Query-Calibrated Operator Compression for Tabular In-Context Learning
Oct 8, 2026Xu Zhao, Jiaming Zhao, Bin Zhao +1Context Compression Tabular ICL
FlowCF: Sparse Counterfactual Explanations for Mixed-Type Tabular Data using Flow Matching
Oct 6, 2026Emmanouil Panagiotou, Eirini NtoutsiFlow Matching Explainable Artificial Intelligence
Freie Universität Berlin · University of the Bundeswehr Munich
Scalable extraction and visualization of multi-attribute logical and functional dependencies in tabular data
Oct 6, 2026Chaithra Umesh, Arvind Lomrore, Neethu D +3Tabular ML
Institute of Computer Science, University of Rostock, Germany · School of Data Science, Indian Institute of Science Education and Research, Thiruvananthapuram, India · Leibniz-Institute for Food Systems Biology, Technical University of Munich, Freising, Germany +1
EasyClassifier: Honest, Reproducible Machine-Learning Classification for Researchers Who Do Not Program
Oct 3, 2026Ahmad B. A. Hassanat, Ghada A. AltarawnehML Reproducibility Classification
Faculty of Information Technology, Mutah University, Karak, Jordan · Department of Accounting, Mutah University, Karak, Jordan
Marginal Response Surface Elicitation for Zero-Label Tabular Learning
Sep 30, 2026Liangyu Teng, Yicheng Ding, Jing Liu +5Tabular Prediction Tabular ML
Fudan Institute on Networking Systems of AI
Hybrid Methods for Robust Tabular Data Imputation
Sep 30, 2026Jinwei Li, Michelle Bruch, Daniel TenbrinckIncomplete Data Imputation Tabular ML
Department of Data Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen, Germany
Large Language Models for Automated Cross-Domain Machine Learning Task Type Identification: A Benchmark Dataset and Evaluation
Sep 28, 2026Petros Tsialis, Steffen Limmer, Tobias Rodemann +1LLM Evaluation Tabular ML
University of Applied Sciences Aalen · Honda Research Institute Europe
Beyond Correctness: Evaluating Semantic Knowledge in Cross-Table Transfer
Sep 28, 2026Seokyong Sheem, Hochang Lee, Suyeong Lee +1Tabular ML Tabular Prediction
Korea University
Adaptive Ensemble Selection for Noisy Labels on Tabular Data
Sep 26, 2026Faizaan Ali, Inwon Kang, Oshani SeneviratneLearning with Noisy Labels Ensemble Learning
Rensselaer Polytechnic Institute Troy, New York, USA
CRISP: Scalable Importance-Stratified Coresets for Imbalanced Tabular Learning
Sep 22, 2026Hardhik Mohanty, Indrayana Rustandi, Mohamadreza SheibaniClass-Imbalanced Learning Training Data Selection
University of Southern California Los Angeles, California, USA · Coinbase San Francisco, California, USA
Machine Learning-Based Prediction of Childhood Stunting in Bangladesh: Fairness and Temporal Robustness Assessment
Sep 21, 2026Md Ahshanul Haque, Muhammad Ashad KabirHealthcare Algorithmic Fairness
School of Computing, Mathematics and Engineering, Charles Sturt University, NSW, Australia
Agentic Search Spaces for Tabular Machine Learning
Sep 14, 2026Renat Sergazinov, Artem Chistyakov, Sergey Pankevich +1Large Language Model-Guided Optimization Tabular ML
† Yandex ‡ HSE University
TabBench-Bio: A Living Benchmark for Machine Learning on High-Dimensional Biomedical Tables
Sep 7, 2026Jules Kreuer, Sofiane Ouaari, Julia Hellmig +2Tabular Foundation Models Tabular ML
Methods in Medical Informatics, University of Tübingen, Tübingen, Germany
Scaling Laws, Tabular Data and Actuarial Ratemaking Models
Sep 2, 2026Ronald RichmanTabular ML Scaling Laws
insureai and Bayes Business School, Johannesburg, South Africa
Solving In-Table Prediction Problems by Deep Neural Networks with Performance Evaluation Using Synthetic Data
Sep 1, 2026Xiao Zhao, Daniela OelkeLearning with Missing Data Tabular ML
Institute for Machine Learning and Analytics Offenburg University · Department of Electrical Engineering, Medical Engineering and Computer Science Offenburg University
Adaptive Multi-Branching for Shallow Decision Tree Induction
Aug 29, 2026Hanul Park, Jeonghoon Choi, Juseong Kim +2Decision Tree Learning Tabular ML
Department of Information Convergence Engineering, Pusan National University, Busan, Rep. of Korea · School of Computer Science and Engineering, Pusan National University, Busan, Rep. of Korea
SymboLLM-FE: LLM-Accelerated Symbolic Regression for Automated Feature Engineering on Tabular Data
Aug 28, 2026Zi-Jian Cheng, Zi-Yi Jia, Zhi Zhou +2Large Language Model-Based Feature Engineering Automated Feature Engineering
National Key Laboratory for Novel Software Technology, Nanjing University · School of Intelligence Science and Technology, Nanjing University · School of Artificial Intelligence, Nanjing University
Generalized Context in Cross Attention for Transfer Learning of Disjoint Tabular Data
Aug 28, 2026Kazi F. Akhter, Ibna Kowsar, Manar D. SamadCross-Domain Transfer Learning Transfer Learning
Department of Computer Science Tennessee State University Nashville, TN, USA · Department of Computer Science North Carolina Agricultural and Technical State University Greensboro, NC, USA
Actionable CBFI: Prescriptive Interventional Diagnosis and Interaction-Aware Escalation for Sparse Causal Algorithmic Recourse
Aug 28, 2026Sejong OhFeature Attribution Counterfactual Explanations
Dankook University, Republic of Korea
Importance Scoring of Transformer Attention Heads in Learning Tabular Data
Aug 27, 2026Ahmad Jad Allah, Kazi F. Akhter, Md. Kamrozzaman Bhuiyan +1Attention Head Analysis Transformer Attention
Department of Computer Science Tennessee State University Nashville, TN, USA · Enosis Solutions Dhaka, Bangladesh · Department of Computer Science North Carolina Agricultural and Technical University Greensboro, NC, USA
Tabular Deep Learning for Algorithmic Trading: Cross-Regime Bayesian Optimisation for Equity Signal Generation
Aug 27, 2026Joshua Le GriceAlgorithmic Trading Tabular ML
Department of Computer Science University of Exeter

Oct 24, 2026J/K move · Enter open · S save

Geodesic-SVDD Latent Mixup for One-Shot Distributed Tabular Learning

Andrea Moleri, Christian Internò, Markus Olhofer, Barbara Hammer+1

Honda Research Institute Europe · University of Milan-Bicocca · Bielefeld University

Federated Learning Tabular ML Mixup One-Class Classification Support Vector Machines

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Distributed Learning on structured data is a key and widely adopted approach in privacy-sensitive sectors, ranging from financial fraud prevention to federated healthcare diagnostics. While Gradient Boosted Decision Trees are widely used in centralized settings due to their efficiency, they are incompatible with standard gradient-based federated learning protocols because of their non-differentiability. Conversely, standard deep classifiers that are amenable to federated optimization often suffer from catastrophic forgetting under label distribution skew. In this work, to address these gaps we propose Geodesic-SVDD, a framework for Distributed Spherical Prototype Learning that recasts each client's task as one-class density estimation on the hypersphere, so that local models can be aggregated as probabilistic manifolds in a single round. Specifically, for each client, we employ a Residual Spherical Encoder and Geodesic Latent Mixup that enforces strict hyperspherical constraints (

\|φ(x)\| \equiv 1

), preventing the manifold collapse observed in linear approximations. Furthermore, leveraging this insight, we propose a novel One-Shot "Product of Experts" protocol that enables privacy-preserving aggregation without iterative synchronization. Finally, we mechanistically analyze the trade-off between interpolation strategies, demonstrating that while linear mixup offers an enticing computational shortcut, it destabilizes optimization gradients by orders of magnitude (

10^9\times

). Across five tabular benchmarks and four levels of label skew, Geodesic-SVDD attains the highest mean AUROC among seven federated methods, outperforms federated tree ensembles by up to 21 AUROC points on Covertype, and its One-Shot variant matches centralized Deep SVDD under IID and mild skew with a single communication round.