Tabular Anomaly Detection

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Overview

Tabular anomaly detection seeks unusual records in structured datasets, where anomalies may be rare, labels scarce, and features numeric, categorical, or mixed. Common approaches include isolation forests, neighborhood and density scores, reconstruction and energy models, and newer diffusion-based methods; some combine global and local evidence. Recent work also explores pretrained tabular models, in-context learning, synthetic anomalies, and adaptation across datasets to reduce the need for dataset-specific training. A central challenge is making detectors reliable under contaminated training data, distribution shifts, and different anomaly rates while choosing useful thresholds and producing understandable explanations.

Latest papers

CardsList

29 papers6 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

  1. Universal Semantic Enhancement for Weakly Supervised Anomaly Detection

    Oct 16, 2026Siyu Zhou, Yuqin Xie, Hao Yuan +1Tabular Anomaly DetectionAnomaly Detection

    Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong · College of Computer Science, Sichuan University, China

  2. Moment-Based Anomaly Detection in High-Dimensional Mixed Data

    Oct 12, 2026Davide Ferrari, Andrea NicolodiAnomaly DetectionTabular Anomaly Detection

    Free University of Bozen–Bolzano, Italy · University of Trento, Italy

  3. Adapting prior-data fitted networks for tabular anomaly detection

    Oct 5, 2026Maximilian Bershtman, Niv CohenTabular Anomaly DetectionUnsupervised Anomaly Detection

    Faculty of Electrical and Computer Engineering Technion – Israel Institute of Technology Haifa, Israel

  4. Understanding Errors in LLM-Based Question Answering over Imperfect Tables

    Oct 3, 2026Baowen Zhang, Wei Fan, Ruman Wang +1Table QALanguage Model Error Detection

    University of Wisconsin–Madison · University of Auckland, Auckland, New Zealand · Liaoning Provincial People’s Hospital +1

  5. Mitigating Convergence Collapse in Fixed-Target Anomaly Detectors via Kernel-Anchored Locality Regularization

    Oct 1, 2026José Lucas De Melo Costa, Fabrice Popineau, Arpad Rimmel +1Anomaly DetectionTabular Anomaly Detection

    Université Paris-Saclay, CNRS, CentraleSupélec, LISN UMR 9015 Orsay, France · Université Paris-Saclay, CNRS, CentraleSupélec, Laboratoire Interdisciplinaire des Sciences du Numérique Orsay, France

  6. TaskBridge: Bridging Unsupervised Tabular Anomaly Detection and In-Context Learning via Virtual Tasks

    Sep 29, 2026Doyun Choi, Dooho Lee, Jaemin YooTabular Anomaly DetectionZero-Shot Anomaly Detection

    Seoul National University · KAIST · Nums AI Inc.

  7. Differentiable Interval Bottlenecks for Interpretable Anomaly Detection in Numerical Data

    Sep 3, 2026Lamine Diop, Marc PlantevitTabular Anomaly DetectionAutoencoders

    EPITA, Laboratoire de Recherche de l’EPITA (LRE) · Le Kremlin-Bicˆetre, France

  8. Witnesses Explain Anomalies

    Sep 3, 2026Lamine DiopFeature AttributionTabular Anomaly Detection

    EPITA Research Laboratory, Le Kremlin-Bicˆetre FR-94276, France

  9. When Identical Rows Disagree: From Benchmark Identifiability to Replication-Robust Anomaly Detection

    Aug 27, 2026Jie DengTabular Anomaly DetectionUnsupervised Anomaly Detection

  10. Unsupervised Anomaly Detection Using Flow Matching on Tabular Data

    Aug 20, 2026Philip Konz, Tejaswini Medi, Margret KeuperUnsupervised Anomaly DetectionAnomaly Detection

    University of Mannheim, Germany · MPI for Informatics, Saarland Informatics Campus, Germany

  11. LLM as Detector: An In-context Learning Approach for Tabular Anomaly Detection

    Aug 19, 2026Tu Anh Hoang Nguyen, Dang Nguyen, Thuc Duy Le +2Tabular Anomaly DetectionAnomaly Detection

    Applied Artificial Intelligence Initiative (A2I2), Deakin University, Australia · Adelaide University, Australia · Monash University, Australia

  12. Revisiting Energy-based Tabular Anomaly Detection: Energy and Reconstruction are Complementary

    Aug 14, 2026Junichiro NiimiTabular Anomaly DetectionEnergy-Based Models

    Meijo University, Nagoya Aichi 4688502, Japan

  13. CORE: In-Context Reconstruction for Unified Tabular Anomaly Detection

    Jul 30, 2026Yunfeng Zhao, Qingfeng Chen, Yue Tan +4Tabular Anomaly DetectionReconstruction-Based Anomaly Detection

    1Guangxi University · 2Griffith University · 3Jilin University

  14. Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation

    Jul 21, 2026Rahil SharmaAlgorithmic AuditingTabular Anomaly Detection

    Vrije Universiteit Amsterdam

  15. FlexTab: A Flexible Encoder-Decoder Architecture for In-Context Learning Across Diverse Tabular Tasks

    Jun 29, 2026Marek Polewczyk, Maximilian Schambach, Marco Spinaci +2Tabular Foundation ModelsTabular Anomaly Detection

    SAP SE · SAP France

  16. RGLD: Randomized Global-Local Density Estimation for Tabular Anomaly Detection

    Jun 27, 2026Quanling Zhao, Jiaying Yang, Ye Tian +5Tabular Anomaly DetectionUnsupervised Anomaly Detection

    1Computer Science and Engineering, University of California San Diego · 2Intel Corporation

  17. TaskFusion: Continual Anomaly Detection for Heterogeneous Tabular Data

    Jun 10, 2026Dayananda Herurkar, Federico Raue, Joachim Folz +2Continual Anomaly DetectionTabular Anomaly Detection

    German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany · RPTU Kaiserslautern-Landau, Germany · Hochschule Bonn-Rhein-Sieg (H-BRS), Germany

  18. Masked Diffusion Modeling for Anomaly Detection

    May 28, 2026Lixing Zhang, Yuchen Liang, Liyan XieMasked Diffusion ModelsTabular Anomaly Detection

    University of Minnesota · Ohio State University

  19. SilIF: Silhouette-Augmented Isolation Forest for Unsupervised Transaction Fraud Detection

    May 21, 2026Venkatakrishnan GopalakrishnanTabular Anomaly DetectionIsolation Forest

    Independent Researcher Redmond, WA, USA

  20. When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection

    May 11, 2026Wei Huang, Hezhe Qiao, Kailai Zhang +3Test-Time AdaptationTabular Anomaly Detection

  21. Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection

    May 7, 2026Victor Livernoche, Jie Zan, Reihaneh RabbanyTabular Anomaly DetectionUnsupervised Anomaly Detection

    McGill University, Mila, Montréal, Canada

  22. Unsupervised Machine Learning for Detecting Structural Anomalies in European Regional Statistics

    May 4, 2026Bogdan OanceaTabular Anomaly DetectionUnsupervised Anomaly Detection

    Department of Applied Economics and Quantitative Analysis, University of Bucharest, Romania · National Institute for Research and Development for Biological Sciences, Bucharest, Romania

  23. uLEAD-TabPFN: Uncertainty-aware Dependency-based Anomaly Detection with TabPFN

    Apr 22, 2026Sha Lu, Jixue Liu, Stefan Peters +4Tabular Anomaly DetectionAnomaly Detection

    Adelaide University · Queen's University Belfast

  24. Enhancing Tabular Anomaly Detection via Pseudo-Label-Guided Generation

    Apr 20, 2026Wei Huang, Yuxuan Xiong, Hezhe Qiao +3Tabular Anomaly DetectionPseudo-Labeling

  25. ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes

    Mar 19, 2026Jack Yi Wei, Narges ArmanfardTabular Anomaly DetectionTabular Foundation Models

    Department of Electrical and Computer Engineering, McGill University, Canada · Mila - Quebec Artificial Intelligence Institute, Montreal, Canada

  26. Towards One-for-All Anomaly Detection for Tabular Data

    Mar 15, 2026Shiyuan Li, Yixin Liu, Yu Zheng +3Tabular Anomaly DetectionAnomaly Detection

    Griffith University, Gold Coast, Australia · Tongji University, Shanghai, China

  27. TACTIC for Navigating the Unknown: Tabular Anomaly deteCTion via In-Context inference

    Mar 15, 2026Patryk Marszałek, Tomasz Kuśmierczyk, Marek ŚmiejaTabular Anomaly DetectionAnomaly Detection

    Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland · Doctoral School of Exact and Natural Sciences, Jagiellonian University, Kraków, Poland

  28. Why Does Counterintuitive Likelihood Behavior Rarely Emerge in Tabular Anomaly Detection?

    Feb 10, 2026Donghwan Kim, Junghun Phee, Hyunsoo YoonAnomaly DetectionTabular Anomaly Detection

    Department of Industrial Engineering, University of Yonsei, Republic of Korea, Seoul · H. Milton Stewart School of Industrial and Systems Engineering (ISyE), Georgia Institute of Technology, USA, GA

  29. Isolation-based Spherical Ensemble Representations for Tabular Anomaly Detection

    Oct 15, 2025Yang Cao, Sikun Yang, Hao Tian +5Tabular Anomaly DetectionIsolation Forest

    Tsinghua Shenzhen International Graduate School, Tsinghua University, China · School of Computing and Information Technology, Great Bay University, China · School of Compuer Science, Nanjing University, China +3