Tabular ML

ML: Machine Learning

Momentum

7 papers in the last four weeks, up 17% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 72

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  1. FlowCF: Sparse Counterfactual Explanations for Mixed-Type Tabular Data using Flow Matching

    Oct 6, 2026Emmanouil Panagiotou, Eirini NtoutsiFlow MatchingExplainable Artificial Intelligence

  2. Scalable extraction and visualization of multi-attribute logical and functional dependencies in tabular data

    Oct 6, 2026Chaithra Umesh, Arvind Lomrore, Neethu D +3Tabular ML

  3. Hybrid Methods for Robust Tabular Data Imputation

    Sep 30, 2026Jinwei Li, Michelle Bruch, Daniel TenbrinckIncomplete Data ImputationTabular ML

  4. 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 EvaluationTabular ML

  5. Beyond Correctness: Evaluating Semantic Knowledge in Cross-Table Transfer

    Sep 28, 2026Seokyong Sheem, Hochang Lee, Suyeong Lee +1Tabular MLTabular Prediction

  6. CRISP: Scalable Importance-Stratified Coresets for Imbalanced Tabular Learning

    Sep 22, 2026Hardhik Mohanty, Indrayana Rustandi, Mohamadreza SheibaniClass-Imbalanced LearningTraining Data Selection

  7. Machine Learning-Based Prediction of Childhood Stunting in Bangladesh: Fairness and Temporal Robustness Assessment

    Sep 21, 2026Md Ahshanul Haque, Muhammad Ashad KabirHealthcareAlgorithmic Fairness

  8. Agentic Search Spaces for Tabular Machine Learning

    Sep 14, 2026Renat Sergazinov, Artem Chistyakov, Sergey Pankevich +1Large Language Model-Guided OptimizationTabular ML

  9. TabBench-Bio: A Living Benchmark for Machine Learning on High-Dimensional Biomedical Tables

    Sep 7, 2026Jules Kreuer, Sofiane Ouaari, Julia Hellmig +2Tabular Foundation ModelsTabular ML

  10. Scaling Laws, Tabular Data and Actuarial Ratemaking Models

    Sep 2, 2026Ronald RichmanTabular MLScaling Laws

  11. Solving In-Table Prediction Problems by Deep Neural Networks with Performance Evaluation Using Synthetic Data

    Sep 1, 2026Xiao Zhao, Daniela OelkeLearning with Missing DataTabular ML

  12. TabSOM: A tabular-to-image encoding method based on self-organizing maps

    Aug 13, 2026David Chushig-Muzo, María Ángeles Rodríguez de Cara, Eva Milara +3Tabular MLSelf-Organizing Maps

  13. Unlocking the Power of Medical Tabular Data via Semantic-Aware Multimodal Pre-training

    Aug 11, 2026Yingsheng Liu, Haiming Li, Jingmin Zhu +6Cross-Modal Representation LearningMultimodal Pretraining

  14. Tabular Numeric Stretch Transformation

    Aug 10, 2026Zihao Ye, Juyong Kim, Johnna Sundberg +2Tabular MLAutomated Feature Engineering

  15. GRACE: LLM-Grounded Semantic Metric Spaces for Scalable Mixed-Data Clustering

    Aug 8, 2026Zihua Yang, Zhencheng Xie, Junyang Chen +4ClusteringUnsupervised Clustering

  16. NOMADD: Numerical Optimization of Models Adapting to Data Drift

    Aug 3, 2026Swapn Shah, Keith BurghardtConcept DriftTabular ML

  17. Logit-Origin Centering for Singleton Test-Time Adaptation

    Aug 2, 2026Mayank Sharma, Rohit Kumar Mourya, Pratik MazumderTest-Time AdaptationTabular ML

  18. Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data

    Jul 31, 2026Pulock Das, Yina Hou, Md. Kamrozzaman Bhuiyan +1Class-Imbalanced LearningClustering

  19. Adaptive Graph-of-Islands Evolution for Automatic Feature Engineering with LLMs

    Jul 25, 2026Sha Li, Naren RamakrishnanEvolutionary OptimizationLarge Language Model-Guided Optimization

  20. Is the Statistical Advantage Worth the Cost? An Empirical Comparison of KANs and MLPs for Structured Data Classification

    Jul 15, 2026Matthew Steven P. Toledo, Justine Raphael H. Jacinto, Vivekjeet Singh Chambal +3Multilayer PerceptronsTabular Classification

  21. Inter-Stop Energy Prediction and Causal Driver Quantification for Dual-Source Trolleybuses via a Time-Aware Tabular Deep Learning Architecture

    Jul 13, 2026Wentao Zeng, Zijian Huang, Yiming Bie +2Time Series ForecastingTabular ML

  22. ProgramTab: Boosting Table Reasoning of LLMs via Programmatic Paradigm

    Jul 13, 2026Pei Guo, Enjie Liu, Yunzhi Tan +6Tabular MLLLM Reasoning

  23. TabPack: Efficient Hyperparameter Ensembles for Tabular Deep Learning

    Jul 6, 2026Yury Gorishniy, Akim Kotelnikov, Ivan Rubachev +1Multilayer PerceptronsEnsemble Learning

  24. Evolutionary Feature Engineering for Structured Data

    Jul 2, 2026Ege Onur Taga, Yilin Zhuang, M. Emrullah Ildiz +4Large Language Model-Guided OptimizationTime Series Forecasting

  25. Interpretable vs Learned Encoders for High-Cardinality Fraud Detection

    Jul 1, 2026Xiao Han, Jingjing Liu, Moxuan Zheng +2Financial Fraud DetectionTabular ML

  26. Exploring Differences Between Tabular Enterprise Data and Public Benchmarks

    Jun 29, 2026Myung Jun Kim, Maximilian Schambach, Frank Essenberger +2Benchmark DesignTabular ML

  27. Beyond IID: How General Are Tabular Foundation Models, Really?

    Jun 29, 2026Lennart Purucker, Andrej Tschalzev, Nick Erickson +7Tabular Foundation ModelsOOD Generalization

  28. Interpretable Concept-Guided Polynomial Tabular Kolmogorov-Arnold Network for EEG-Based Mild Cognitive Impairment Detection

    Jun 24, 2026Yosef Bernardus Wirian, Qiang ChengElectroencephalographyInterpretable ML

  29. When, Where, and How: Adaptive Binning for Tabular Self-Supervised Learning

    Jun 18, 2026Daehwan Kim, Haejun Chung, Ikbeom JangSelf-Supervised LearningTabular ML

  30. Parameter-Efficient Adapter Tuning for Tabular-Image Multimodal Learning

    Jun 10, 2026Jiaqi LuoFine-TuningAdapter Tuning

  31. Towards Pretraining Text Encoders for TabPFN

    Jun 3, 2026Mustafa Tajjar, Alexander Pfefferle, Lennart Purucker +1Tabular Foundation ModelsFine-Tuning

  32. TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks

    Jun 1, 2026Andrej Tschalzev, Nick Erickson, Yuyang Wang +4Tabular MLAutomated Feature Engineering

  33. Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation

    Jun 1, 2026Woojun Jung, Susik YoonTabular Foundation ModelsSelf-Supervised Pre-Training

  34. Profiling Privacy Preservation Against Gradient Inversion Attacks in Tabular Federated Learning

    May 31, 2026Ivo Osterberg Nilsson, Maximilian Birr Engvall, Viktor Valadi +1Gradient Inversion AttacksAdversarial Attacks

  35. TabChange: Precise Attribute Changes in Tabular Data

    May 30, 2026Arjun Dahal, Yu Lei, Raghu N. Kacker +1Adversarial Representation LearningCounterfactual Explanations

  36. Open World Autoencoding Drift Detection with Novel Class Recognition in Tabular Non-stationary Data Streams

    May 28, 2026Joanna KomorniczakConcept DriftAutoencoders

  37. Explaining Tabular Foundation Model Differences Through Meta-Features

    May 27, 2026Markus Herre, Andrej Tschalzev, Sascha Marton +1Tabular Foundation ModelsTabular ML

  38. Evaluating Local Explainability Metrics for Machine Learning Models on Tabular Data

    May 26, 2026Tomás Pereira, João Vitorino, Eva Maia +1Feature AttributionExplainable Artificial Intelligence

  39. Trajectory-Based Difficulty Scoring for Reliable Learning on Tabular Data

    May 23, 2026Tomer Lavi, Bracha Shapira, Nadav RappoportGradient-Boosted Decision TreesTabular ML

  40. Machine-Learning-Enhanced Non-Invasive Testing for MASLD Fibrosis: Shallow-Deep Neural Networks Versus FIB-4, Tabular Foundation Models, and Large Language Models

    May 19, 2026Athanasios Angelakis, Gabriele De Vito, Eleni-Myrto Trifylli +1Shallow Neural NetworksTabular ML

  41. Multi-Dimensional Model Integrity and Responsibility Assessment Index and Scoring Framework

    May 14, 2026Phuc Truong Loc Nguyen, Thanh Hung Do, Truong Thanh Hung Nguyen +1Tabular MLResponsible AI

  42. STRABLE: Benchmarking Tabular Machine Learning with Strings

    May 12, 2026Gioia Blayer, Myung Jun Kim, Félix Lefebvre +8Tabular MLTabular Prediction

  43. ASD-Bench: A Four-Axis Comprehensive Benchmark of AI Models for Autism Spectrum Disorder

    May 11, 2026Shubhankit Singh, Hassan Shaikh, Kuldeep Raghuwanshi +1Probability CalibrationAutism Spectrum Disorder

  44. SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data

    May 8, 2026Kacper Jurek, Wojciech Batko, Marek Śmieja +1Multi-View LearningFew-Shot Learning

  45. Approximation-Free Differentiable Oblique Decision Trees

    May 8, 2026Subrat Prasad Panda, Blaise Genest, Arvind EaswaranInterpretable MLTabular ML

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

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

  47. TabEmbed: Benchmarking and Learning Generalist Embeddings for Tabular Understanding

    May 6, 2026Minjie Qiang, Mingming Zhang, Xiaoyi Bao +5Tabular Foundation ModelsTabular ML

  48. ITBoost: Information-Theoretic Trust for Robust Boosting

    May 6, 2026Ye Su, Longlong Zhao, Diego Garcia-Gil +4Gradient-Boosted Decision TreesEnsemble Learning

  49. TabSurv: Adapting Modern Tabular Neural Networks to Survival Analysis

    May 5, 2026Stanislav Kirpichenko, Andrei Konstantinov, Lev UtkinSurvival AnalysisDeep Ensembles

  50. DynaTab: Dynamic Feature Ordering as Neural Rewiring for High-Dimensional Tabular Data

    May 5, 2026Al Zadid Sultan Bin Habib, Gianfranco Doretto, Donald A. AdjerohDynamic Neural NetworksTabular ML