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

• AccreditNet: An Explainable Data-Adaptive AI Framework for Accreditation Quality Analytics and Decision Support in the Training Sector
  Fahd Aldosari, Donia Y. Badawood, Khaled H. Almotairi · Algorithms · Sep 10, 2026

  Accreditation and quality assurance in technical and vocational education and training (TVET) remain largely dependent on manual review and expert judgment. This study presents AccreditNet, an explainable tabular AI model embedded in a broa…

• A Comparative Study of Construction Site Accident Risk Screening Models Using Public Construction Project Data - Focusing on Tabular Machine Learning and Attribute-Based Heterogeneous Graph Models -
  Seokhyeon Moon, Sangil Yoon, SangHwan Kim, YongHan Ahn et al. · Korean Journal of Computati... · Sep 10, 2026

  This study compares tabular machine learning models and attribute-based heterogeneous graph models for construction site accident risk screening using 214,293 public construction project records with a 0.91% accident rate. Six models were c…

• Interpretable TabPFN-Based prediction of unconfined compressive strength in chemically stabilized soft soils for transportation subgrade applications
  Quynh-Anh Thi Bui, Son Hoang Trinh, Khoa Minh Nguyen · Journal of Engineering and ... · Sep 10, 2026

  Abstract Chemical stabilization is widely used to improve soft soils for road embankments and transportation subgrades in soft-ground regions. However, predicting their unconfined compressive strength (UCS) remains difficult because strengt…

• A Hybrid Ensemble for Early Flood Risk Forecasting Using Multimodal Spatiotemporal Data
  Assylzat Slanbekova, Madi Akhmetzhanov, Leyla Fazylova, Shynar Turmaganbetova et al. · Computers · Sep 10, 2026

  This study presents a leakage-proof hybrid machine learning framework for early flood risk forecasting using multimodal spatiotemporal tabular data. An event-based dataset covering Kazakhstan from 2001 to 2021 was created by integrating top…

• Quantum Feature Engineering for Credit Default Prediction: When and Why IQP Circuits Help Linear Classifiers
  Menachem Finkelstein, Diana Legziel Levy, Zohar Yakhini, Sarel Cohen · arXiv · Sep 9, 2026

  Credit default prediction is a tabular classification problem in which modest gains in F1 translate directly into reduced financial exposure. We ask whether Instantaneous Quantum Polynomial-time (IQP) circuits can produce features that impr…

• Enhancing class-imbalanced soil classification using an explainable tabular network and synthetic minority over-sampling framework
  Yared Bitew Kebede, Henok Desalegn Shikur, Ming‐Der Yang · Engineering Applications of... · Sep 9, 2026

  Accurate characterization of soil engineering properties is fundamental to effective infrastructure design; however, traditional soil classification approaches are resource-intensive, requiring extensive laboratory testing which affects roa…

• AI POWERED NATURAL LANGUAGE TO SQL QUERY SYSTEM FOR CSV DATA ANALYSIS
  Mrs. Nandhini S, Nandhini K, Nandhini K, Naveen K et al. · Zenodo (CERN European Organ... · Sep 7, 2026

  The rapid growth of data across various domains has increased the need for efficient and user-friendly data analysis tools. However, extracting meaningful insights from structured datasets often requires knowledge of Structured Query Langua…

• AI POWERED NATURAL LANGUAGE TO SQL QUERY SYSTEM FOR CSV DATA ANALYSIS
  Mrs. Nandhini S, Nandhini K, Nandhini K, Naveen K et al. · Zenodo (CERN European Organ... · Sep 7, 2026

  The rapid growth of data across various domains has increased the need for efficient and user-friendly data analysis tools. However, extracting meaningful insights from structured datasets often requires knowledge of Structured Query Langua…

• SecureLite: Lightweight Vulnerability Detection on CVEFixes with TF‑IDF, Gradient Boosting, and a Tiny Transformer plus LLM‑Assisted Triage
  Qi Xin · Data Science Journal of Com... · Sep 7, 2026

  Static application security testing remains difficult to deploy at scale in Python repositories because practical tools must be accurate, fast, and interpretable under extreme class imbalance. This paper introduces SecureLite, a lightweight…

• A Novel Tabular-to-Image Conversion Approach for Frontotemporal Dementia Progression Forecasting using Hybrid CNN-RNN Models
  Km Poonam, Venkata Sathwik Kotra, Rajlakshmi Guha, P. P. Chakrabarti · ACM Transactions on Computi... · Sep 6, 2026

  Frontotemporal Dementia (FTD) presents significant challenges in disease progression forecasting due to its complex temporal dynamics and the logistical difficulties of acquiring longitudinal imaging data. Convolutional Neural Networks (CNN…

• "Reproducibility package: Predictive Similarity Does Not Imply Selection Reproducibility"
  Francisco Ndong Ndong Obono, Zhang Jue · Zenodo (CERN European Organ... · Sep 6, 2026

  Feature selection and class-imbalance handling are routinely combined in medical tabular classification, yet judged by predictive performance alone. Whetherresampling changes the reproducibility of the selection itself is left unmeasured, a…

• "Reproducibility package: Predictive Similarity Does Not Imply Selection Reproducibility"
  Francisco Ndong Ndong Obono, Zhang Jue · Zenodo (CERN European Organ... · Sep 6, 2026

  Feature selection and class-imbalance handling are routinely combined in medical tabular classification, yet judged by predictive performance alone. Whetherresampling changes the reproducibility of the selection itself is left unmeasured, a…

• Attention-Enhanced Autoencoder with Marginal-Variance-Regularized Feature Reconstruction for Imbalanced Insurance Policy-Ownership Classification
  Jiaming Tian, Qingyi Ding, Bohan Li, Xiao Yang · Entropy · Sep 3, 2026

  Identifying the small group of customers who hold a given policy in severely imbalanced tabular data is a recurring screening problem in insurance analytics. This study considers binary caravan-insurance policy-ownership classification on t…

• IFC-HFlowVAE: A self-enhancing generative framework with structural anchoring for imbalanced clinical data augmentation
  Lu Yuwen, Shuyu Chen · PLoS ONE · Sep 3, 2026

  Class imbalance and limited minority-class samples remain major challenges for developing reliable clinical diagnostic models, as insufficient observations often fail to capture complex minority-class distributions. Existing augmentation me…

• Do Tabular Foundation Models Know Physics? Contamination, Units, and the Deterministic Limit
  Wassim Tenachi, Yashar Hezaveh, Laurence Perreault Levasseur, Pierre-Luc Bacon · arXiv · Sep 2, 2026

  Tabular foundation models (TFMs) learn to fill in tables the way language models fill in text, and tables are arguably the format in which most physical measurement arrives. Did they learn any physics in the process? They are Bayesian by co…

• LoRA-TSD: Tangent-Space Spectral Descent for LoRA via Muon-Style Updates
  Dmitrii Andriianov, Andrey Veprikov, Aleksandr Beznosikov · arXiv · Sep 2, 2026

  Low-rank adaptation (LoRA) is the standard way to fine-tune large models, yet when its two factors are trained independently, the update ignores the geometry of the low-rank weight change it induces. We introduce LoRA-TSD, an optimizer that…

• Improving Multimodal Atmospheric Visibility Estimation Using Modern Image Feature Extractors and Attention-Based Fusion
  Petr Doležel, Dominik Štursa, Dušan Kopecký, Jitka Kopecká · Zenodo (CERN European Organ... · Sep 2, 2026

  Atmospheric visibility estimation is important for transport safety, environmental monitoring, and other operational applications. A previously proposed multimodal model combined RGB camera images with meteorological variables and showed th…

• Improving Multimodal Atmospheric Visibility Estimation Using Modern Image Feature Extractors and Attention-Based Fusion
  Petr Doležel, Dominik Štursa, Dušan Kopecký, Jitka Kopecká · Zenodo (CERN European Organ... · Sep 2, 2026

  Atmospheric visibility estimation is important for transport safety, environmental monitoring, and other operational applications. A previously proposed multimodal model combined RGB camera images with meteorological variables and showed th…

• AISyst: AI‐Powered Interactive Visual System to Assist With Fidelity Assessment of Synthetic Tabular Data
  L. Liu, L. Bogachev, N. Onyiaji, L. Čironis et al. · White Rose Research Online ... · Sep 1, 2026

  Evaluating synthetic data produced by generative models remains a critical challenge in sensitive domains such as healthcare and finance. Ensuring that such data is ‘faithful’ to real data is essential for downstream applications and decisi…

• TSPFN: A Temporal Tabular Foundation Model for Physiological Time Series Classification
  Jérémie Stym-Popper, Clément Rambour, Federica Granese, Nicolas Thome et al. · arXiv · Aug 31, 2026

  Designing models that generalize effectively in low- to medium-data regimes remains a primary challenge in medical machine learning, particularly for physiological time-series classification. While tabular foundation models such as TabPFN o…

• Generative Distribution Prediction: A Unified Approach to Multimodal Learning
  Xinyu Tian, Xiaotong Shen · Machine Learning · Aug 31, 2026

  Abstract Accurate prediction for multimodal data—including tabular, textual, and visual inputs or outputs—is essential for advancing analytics across diverse application domains. Existing methods often struggle to integrate heterogeneous da…

• SymboLLM-FE: LLM-Accelerated Symbolic Regression for Automated Feature Engineering on Tabular Data
  Zi-Jian Cheng, Zi-Yi Jia, Zhi Zhou, Yu-Feng Li et al. · arXiv · Aug 28, 2026

  Tabular data, as a core data format in machine learning, often lacks the discriminative power needed for high-performance modeling due to insufficient feature informativeness. Automated Feature Engineering (AutoFE) overcomes this by automat…

• General OOD detection via model-aware and subspace-aware variable priority
  Min Lü, Hemant Ishwaran · Knowledge and Information S... · Aug 28, 2026

  Abstract Out-of-distribution (OOD) detection flags test inputs that depart from the data used to train a model. For structured tabular problems with regression or survival outcomes, existing methods remain limited because many OOD detectors…

• A Finite Sample Analysis for Quantile Temporal Difference Learning in Distributional Reinforcement Learning
  Zijie Cheng, Xiang Li, Yang Peng, Zhihua Zhang · arXiv · Aug 27, 2026

  We establish a global finite-sample guarantee for synchronous quantile temporal-difference learning (QTD) in tabular distributional reinforcement learning. The proof separates two stability mechanisms. A global comparison argument, based on…

• Importance Scoring of Transformer Attention Heads in Learning Tabular Data
  Ahmad Jad Allah, Kazi F. Akhter, Md. Kamrozzaman Bhuiyan, Manar D. Samad · arXiv · Aug 27, 2026

  Computationally demanding and opaque deep learning models can be better understood and optimized by analyzing how they transform data. While deep transformers have been widely studied in computer vision and natural language processing, thei…

• Physics-informed Multi-task Tabular Transformer for Joint Fault Prediction and Remaining Useful Life Estimation of Industrial Equipment
  Junling Hu · Applied Artificial Intellig... · Aug 26, 2026

  Accurate fault prediction and remaining useful life (RUL) estimation are essential for predictive maintenance of industrial equipment. However, existing methods mainly focus on time-series signals and often ignore the heterogeneous characte…

• Research on a prediction model for asphalt pavement deflection in full-scale test track integrating multi-source features and machine learning
  Meiyi Zhang, Guiyu Gan, Taixin Fu, Xiangbing Gong et al. · Frontiers in Materials · Aug 26, 2026

  To ensure asphalt pavement durability under environmental and traffic loads, this study investigates permanent deformation caused by temperature changes and repeated axle loads. Based on long-term full-scale track monitoring, a feature fusi…

• Integrating Large Language Models (LLMs) with Oracle 26AI for Advanced Enterprise Analytics and Knowledge Management
  Krishna Kompalli · Journal of Knowledge Learni... · Aug 25, 2026

  Enterprise organizations increasingly hold two categories of information assets that have historically been managed by incompatible systems: structured relational data governed by transactional database platforms, and unstructured knowledge…

• Interpretable AI with Local Distillation
  Erin Craig, Yiling Huang, Snigdha Panigrahi · arXiv · Aug 24, 2026

  Modern AI models such as tabular foundation models and gradient-boosted ensembles can outpredict classical methods, but provide little basis for reasoning about their predictions. High-stakes decisions call for models that are both accurate…

• KellyBoost: Growth-Optimal Portfolio Construction with Gradient-Boosted Trees
  Jiayu Li · arXiv · Aug 24, 2026

  KellyBoost is a single multi-output XGBoost model whose softmax output is the portfolio: with y the vector of per-asset holding-period returns, the training loss is - log(1 + w y), the negative log growth rate, so the fitted model is the gr…