Latest Tabular Data Research Papers

The newest Tabular Data papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Tabular Data so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• აჭარის საარქივო სამმართველოს ცენტრალური სახელმწიფო არქივი, ფონდი რ- 178, საქმე N 29 ქობულეთის მუნიციპალიტეტის 1923 წლის სასოფლო-სამეურნეო აღწერის მონაცემები
  Ajara Archives Administration, აჭარის საარქივო სამმართველო · ARCHEION · Oct 30, 2026

  The publication of documents held in the collections of the Archives Administration of Ajara is a key aspect of archival work. Here, we present to the reader a selection of materials from Case No. 29 of collection R-178, which records the r…

• Homebrewed SLMs: Autoregressive and Masked Diffusion Language Models Built at Home for Portuguese
  Pedro Carvalho Brom · arXiv · Oct 29, 2026

  Building a language model of one's own no longer requires a data centre. This tutorial trains one from scratch, tokenizer included, on one consumer GPU, and shows how to tell whether it is good for the tasks of a specialist domain. It propo…

• Construction cost prediction using TabPFN-based stacking model with SHAP analysis
  Jun Wang, Qiuyan Gu, Nicholas Chileshe · Engineering Construction & ... · Oct 28, 2026

  Purpose Construction cost prediction is essential for improving budget management, risk reduction, and resource allocation in the construction industry. This study aims to enhance prediction accuracy by proposing a stacked ensemble model th…

• Attribute Noise in Tabular Classification: Noise-Matched Training and the Train–Test Noise Mismatch on Public Benchmarks
  Hyuk-Gyu Park, Kwang-Seong Shin, Dong-Hyun Won · Electronics · Oct 27, 2026

  Attribute noise, the measurement error in feature values, is a data-related challenge distinct from label noise, and the noise present at training time rarely equals the noise present at deployment. This article studies this train–test nois…

• Feature-weighted calibrated tabular explainable AI for heart disease prediction
  Muhammad Asad, Md Tohfatul Islam, Md Tanvir Rahman Khan, Eyoab Aregay et al. · Discover Artificial Intelli... · Oct 25, 2026

  Abstract Cardiovascular disease (CVD) remains a leading cause of mortality and morbidity globally. Early detection and stratification of CVD risk using machine-learning models can improve patient outcomes; however, existing models often suf…

• AN INTELLIGENT CONSUMER BEHAVIOUR PREDICTION SYSTEM USING HYBRID MACHINE LEARNING ALGORITHMS
  Ms. Munesh Yadav, Mr. Dilip Kumar, Mr. Praveen Kumar · SRJ International Journal o... · Oct 25, 2026

  Predicting whether a website visitor will make a purchase is one of the most commercially valuable problems in online marketing, yet it is difficult because conversion is rare and visitor behaviour is noisy. This paper presents an intellige…

• A Multimetric Evaluation of Ensemble and Neural Models for Imbalanced Credit Scoring on the German Credit Dataset
  Rino, Aditiya Hermawan, Elfindah Princes, Satria Abadi et al. · Journal of Information Syst... · Oct 24, 2026

  Credit scoring models must balance global discrimination, minority-class detection, probability quality, and computational efficiency, particularly when default cases are less frequent than non-default cases. This study benchmarks Logistic …

• Drivers of supply chain resilience in the new energy vehicle industry: An explainable machine learning approach
  Yang Jiang, Chenchen Li, Jijing Hang · Energy Reports · Oct 24, 2026

  Under the global dual-carbon goals, supply chain resilience has become a strategic priority for the new energy vehicle (NEV) industry. Using a 2016–2023 panel of 403 Chinese listed NEV firms, we construct an entropy-weighted supply chain re…

• Benchmarking Classical Machine Learning and Deep Learning Models on the Cleveland Heart Disease Binary Classification Dataset
  Ajimah Nnabueze Edmund, Ikiomoye Douglas Emmanuel, Esenogho Ebenezer · Journal of Information Syst... · Oct 24, 2026

  Many high-stakes applications relied on binary classification, yet practitioners lacked equitable, methodical benchmarks that evaluated diverse model families under identical conditions. This study used the Cleveland Heart Disease tabular d…

• Nonparametric autoregressive estimation of probability distributions with parallel gradient boosting
  Rémy Chapelle · arXiv · Oct 22, 2026

  Nonparametric estimation of joint probability distributions is a core problem in statistics, which is notoriously challenging with high-dimensional data. Such challenges also arise with conditional distribution estimation and have led to fl…

• Equally accurate, differently explained: Stability of permutation feature importance across machine learning classifiers on clinical tabular data
  Surjeet Dalal, Mohd Akbar · Journal of Artificial Intel... · Oct 22, 2026

  Feature-importance explanations are increasingly used to justify machine learning (ML) models in clinical decision support, yet they are usually reported from a single fitted model without evidence that they would recur if the model were re…

• Improving cognitive impairment classification in Alzheimer’s disease using tabular diffusion augmentation and late fusion deep learning
  Aidan Z Wang, Xiaoxi Shen · Frontiers in Bioinformatics · Oct 22, 2026

  Background Alzheimer’s disease is the most common cause of dementia and is often detected after substantial neurodegeneration has occurred. Distinguishing cognitively impaired individuals from cognitively normal ones using genetic and demog…

• Degradation-informed state-of-health estimation of lithium-ion batteries using a Tabular Prior-data Fitted Network
  Yao Cheng, Xinxin Yue, 但文蛟, Mingyu Wang et al. · Journal of Energy Storage · Oct 21, 2026

  Lithium-ion batteries degrade during service through coupled electrochemical and thermal aging, causing capacity fade and increased operational risk. Accurate state-of-health (SOH) estimation is therefore required for battery health managem…

• When Do LLMs Help with Feature Selection? A Systematic Empirical Study
  Fateme Memar, Dongjie Wang, Arvin Agah · arXiv · Oct 20, 2026

  Large language models (LLMs) can leverage semantic feature names alongside summary statistics for tabular feature selection, yet it remains unclear when such information improves subset selection relative to established statistical methods.…

• End-to-End Machine Learning Automation: A Dual-Route Agentic System for Tabular Data and Time Series
  Qurat-ul-ain Mastoi, Abrar Shahriar, Hassan Osman, Ryan Wilson · OpenAlex · Oct 20, 2026
• Kronecker Delta Rule for Long Context Linear Attention
  Anamaria-Roberta Hartl, Levente Zólyomi, Michael List, Günter Klambauer et al. · arXiv · Oct 19, 2026

  We propose KronLSTM, a linear attention architecture based on the Kronecker Delta Rule, a novel update rule for associative matrix memories. KronLSTM extends the xLSTM family with a memory-revision mechanism that corrects targeted associati…

• On Data Scaling in Tabular ML: More Data, Better Generalization?
  Ivan Rubachev, Nikita Lazarev, Artem Babenko · arXiv · Oct 19, 2026

  Recent work in tabular machine learning has emphasized the importance of diverse and realistic evaluation settings. The niche of large-scale (1M+) data, however, remains under-explored. To study training-data scaling, we assemble eight real…

• Location location location - does a tabular foundation model need spatial features
  Agnaldo Benvenho · Zenodo (CERN European Organ... · Oct 19, 2026

  Mass appraisal models encode the dependence of property prices on location explicitly, through spatial weights matrices, spatial lags, geographically weighted local models or engineered neighbourhood features. We ask whether a tabular found…

• Prediction of Flammability Limits of Methane–Hydrogen Syngas Under High-Temperature and High-Pressure Conditions Using Machine Learning with Advanced Data Sampling Methods
  Seonghyeop Go, Da Hee Kim, Min Chul Lee · Energies · Oct 19, 2026

  Reliable characterization of the upper and lower flammability limits of methane–hydrogen mixtures is imperative for the safe application of hydrogen-enriched fuel systems. However, experimental data under elevated-temperature and elevated-p…

• Simulation-Calibrated Estimation of AUC and Brier Score Evaluation Error in Small Tabular Binary Classification
  Héctor Alejandro Acuña-Cid, Ana Gabriela Galicia Rodríguez, Anayancin Acuña Ruíz, Ana María Herrera Medrano et al. · Data · Oct 19, 2026

  Small tabular datasets can produce uncertain performance estimates because limited sample information affects both model fitting and evaluation. This study developed a procedure calibrated through simulation to estimate the mean absolute di…

• Scatter Plot–Based Tabular-to-Image Conversion Method with a Modified VGG16 Model for Intrusion Detection in IoT Networks
  Zahia Benamor, Ahmed Seghir Zianou, Hemam Mounir · Journal of Information and ... · Oct 18, 2026

  Security is one of the most severe problems in the Internet of Things (IoT) environment. Early detection of attacks is crucial, as it can significantly enhance network security. Convolutional neural networks (CNNs) represent a significant a…

• When SHAP explains a leaked target: a cautionary study of an XGBoost and multi-level SHAP pipeline for S&P 500 direction prediction
  Liansheng Yuan, Yansong Leng, Yuqi Zhang · Scientific Reports · Oct 17, 2026

  Accurate prediction of stock price movements is of substantial importance to investors, portfolio managers, and financial regulators, and although machine learning models are increasingly deployed in quantitative finance, the limited transp…

• Language modeling on tabular data: a survey of foundations, techniques and evolution
  Yucheng Ruan, Xiang Lan, Jingying Ma, Yizhi Dong et al. · Artificial Intelligence Review · Oct 17, 2026

  Tabular data, a prevalent data type across various domains, presents unique challenges due to its heterogeneous nature and complex structural relationships. Achieving high predictive performance and robustness in tabular data analysis holds…

• LLM-Driven Query Planning for Intelligent Access to Structured and Semi-Structured Data
  Mykola Khoroshevskyi · OpenAlex · Oct 17, 2026

  This paper addresses intelligent access to structured and semi-structured data using large language models (LLMs). We develop an approach to LLM-driven query planning in which a user’s information need is handled as a sequence of interdepen…

• Logic-Guided Graph Denoising Diffusion Models
  Samuel Cognolato, Luciano Serafini, Alessandro Sperduti, Andrea Cavallaro · arXiv · Oct 16, 2026

  Graph diffusion models are competitive approaches for generating complex graph-structured data. However, when such distributions have strict structural constraints, diffusion models may fail to capture them exactly, resulting in the generat…

• Universal Semantic Enhancement for Weakly Supervised Anomaly Detection
  Siyu Zhou, Yuqin Xie, Hao Yuan, Yingjie Zhou · arXiv · Oct 16, 2026

  Weakly supervised anomaly detection on tabular data is an essential task in many label-scarce applications, since tabular data is widely encountered and it is commonly observed in practice that only a limited number of labeled anomalies are…

• Psychometric-aware Fusion of Tabular and Item-sequence Representations Using a Stacking Ensemble Meta-learner for Anxiety Severity Classification in Final-year Students
  Nur Arminarahmah, Sri Ayatina Hayati, Muhammad Subhan, Haldi Budiman et al. · International journal of in... · Oct 16, 2026

  Academic, career and social challenges may heighten anxiety among final-year students.In this work, a psychometric-aware stacked ensemble for anxiety-severity classification based on Hamilton Anxiety Rating Scale (HARS) scores was proposed.…

• Composition determination of biodiesel/ethanol blends using dielectric spectroscopy and tabular prior-data fitted networks
  Julián Corach, Martín González, Lautaro Gómez Ciapponi, P.A. Sorichetti · Fuel · Oct 16, 2026

  Biodiesel/ethanol blends represent a promising alternative to reduce the consumption of fossil diesel. This work employs frequency-domain dielectric spectroscopy to determine the permittivity of biodiesel/ethanol blends across the full rang…

• SurvICL: a Foundation Model for in-context Survival Analysis
  Julie Alberge, Linus Bleistein, Judith Abécassis, Marine Le Morvan et al. · arXiv · Oct 15, 2026

  Tabular foundation models (TFMs) now outperform gradient-boosted trees in classification and regression. Yet in survival analysis, which predicts when an event occurs from censored data, often on small clinical cohorts, we find that no exis…

• Self-Supervised One-Class Tabular Deep Learning for Intrusion Detection in Artificial Intelligence of Things Systems
  Thavavel Vaiyapuri, Karthiyayini Murugesan, Yassine Bouteraa · Journal of Computational an... · Oct 15, 2026

  The emergence of the artificial intelligence of things (AIoT) has led to increasingly sophisticated cyberattacks. For many of these threats, labeled training data are scarce or unavailable, which increases the need for label-free intrusion …