Tabular Diffusion Models

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Usefulness of Quantile-Aware Diffusion Modeling for Highly Imbalanced Tabular Data
Oct 5, 2026Abu Talha, Peng Liu, Souradyuti PaulClass-Imbalanced Learning Synthetic Data Augmentation
Indian Institute of Technology Bhilai, Durg, C.G. 491002, India · Loughborough University, Loughborough, LE11 3TU, United Kingdom
CDMD: A Cross-Dataset Mixed-Type Diffusion Model for Tabular Data
Sep 30, 2026Mohamed Amine Ketata, Maximilian Schambach, Stephan GünnemannMasked Diffusion Models Tabular Diffusion Models
Munich Data Science Institute, Technical University of Munich, Germany · SAP SE, Germany
Fast BIB simulation at a future Muon Collider with generative machine learning
Sep 14, 2026Radha Mastandrea, Shiyu Peng, Benjamin Rosser +1Tabular Diffusion Models Generative Modeling
Enrico Fermi Institute, The University of Chicago, Chicago, IL 60637, USA · Data Science Institute, The University of Chicago, Chicago, IL 60637, USA · Department of Physics, Brown University, Providence, RI 02912, USA +1
Conditioning Tree-Based Diffusions and Flows for Probabilistic Tabular Regression
Jul 30, 2026Silas KoemenFlow Matching Gradient-Boosted Decision Trees
Independent Researcher
TabSODA: Tabular Diffusion based Imputation with Skip Pattern Detection and Ordinal Awareness
Jun 3, 2026Yuyu Chen, Taehyo Kim, Hai Shu +1Incomplete Data Imputation Learning with Missing Data
Department of Biostatistics NYU School of Global Public Health
Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness
Jun 3, 2026Lixing Zhang, Yidong Ouyang, Weifu Li +3Incomplete Data Imputation Learning with Missing Data
University of Minnesota · University of California, Los Angeles · Carnegie Mellon University
AugMask: Training Diffusion Models on Incomplete Tabular Data via Stochastic Augmentation and Masking
Jun 2, 2026Jungkyu Kim, Taeyoung Park, Kibok LeeLearning with Missing Data Tabular Diffusion Models
Department of Statistics and Data Science, Yonsei University, South Korea.
Geometry-Aware Tabular Diffusion
May 23, 2026David Turtora ZagardoTabular Diffusion Models Tabular Data Synthesis
Independent Researcher.
FERMI: Exploiting Relations for Membership Inference Against Tabular Diffusion Models
May 12, 2026Abtin Mahyar, Masoumeh Shafieinejad, Yuhan Liu +1Membership Inference Attacks Tabular Diffusion Models
University of Waterloo · Vector Institute
Active Tabular Augmentation via Policy-Guided Diffusion Inpainting
May 11, 2026Zheyu Zhang, Shuo Yang, Bardh Prenkaj +1Data Augmentation Tabular Diffusion Models
Technical University of Munich · Munich Center for Machine Learning (MCML)
On Privacy Leakage in Tabular Diffusion Models: Influential Factors, Attacker Knowledge, and Metrics
May 7, 2026Masoumeh Shafieinejad, D. B. Emerson, Behnoosh Zamanlooy +5Data Leakage Membership Inference Attacks
Vector Institute · McMaster University · University of Toronto +1
Inference-Time Refinement Closes the Synthetic-Real Gap in Tabular Diffusion
May 7, 2026Eugenio Lomurno, Filippo Balzarini, Francesco Benelle +2Diffusion Model Guidance Inference-Time Optimization
Politecnico di Milano, AIRLab, Italy

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Usefulness of Quantile-Aware Diffusion Modeling for Highly Imbalanced Tabular Data

Abu Talha, Peng Liu, Souradyuti Paul

Indian Institute of Technology Bhilai, Durg, C.G. 491002, India · Loughborough University, Loughborough, LE11 3TU, United Kingdom

Class-Imbalanced Learning Synthetic Data Augmentation Tabular Diffusion Models Financial Fraud Detection Diffusion Models

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Classification problem in the context of highly imbalanced data is a major challenge in many real-world applications (e.g., FinTech, healthcare, etc.). In these cases, the vast majority of instances belong to a single class and a small fraction represent the minority class (often the most critical class). Recently, diffusion models have emerged as powerful approaches to reduce the degree of ``imbalanced-ness'' in the dataset; they work by generating synthetic data by capturing complex data distributions using iterative transformations. However, standard diffusion models are not inherently suited to highly skewed or heavy-tailed data, due to inbuilt quadratic error loss, which lacks the structural sensitivity to capture rare, extreme values, and minority-class nuances. We propose a novel approach, namely, Quantile-TabDDPM, based on a quantile-regularized denoising objective that combines the standard quadratic error loss with a quantile loss term to explicitly capture rare events while preserving the theoretical grounding of the original denoising objective. We extensively evaluated our approach on a real-world credit card transaction dataset characterized by extreme class imbalance. The results demonstrate that the integration of diffusion-based synthetic data generation with a quantile-regularized denoising objective provides a robust and effective framework for fraud detection in highly imbalanced datasets.

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