Papers Tabular Data Generation
“Tabular Data Generation” 태그가 달린 논문 112편 · 필터 해제
Generating Benchmark Health Data Using a Tabular Diffusion Transformer
Cross-Tabular Data Generation (CTDG) seeks to learn a generative model from multiple heterogeneous tables and produce new synthetic tabular datasets. However, existing synthetic tabular data generation methods are largel…
Tabular Data GenerationTDGT: A Tabular Data Generation Toolkit supporting adaptive GPU-accelerated Bayesian mixture models, diffusion-based models, and latent-space generative modeling
The growing demand for privacy-preserving data sharing has positioned synthetic data generation as a critical component of responsible AI workflows. Despite notable advances in generative modeling, existing solutions oft…
Synthetic Data GenerationTabular Data GenerationPSyGenTAB: A Privacy-Preserving Framework for Synthetic Clinical Tabular Data Generation via Constrained Optimization
The development of medical AI is constrained by limited access to high-quality clinical data due to institutional silos and strict privacy regulations such as HIPAA and GDPR. Synthetic data generation offers a potential …
Synthetic Data GenerationTabular Data GenerationBSTabDiff: Block-Subunit Diffusion Priors for High-Dimensional Tabular Data Generation
High-Dimensional Low-Sample Size (HDLSS) tabular domains (e.g., omics) are characterized by $n \ll m$, where $n$ = number of samples, and $m$ = number of features. Such domains often exhibit strong local correlation grou…
Tabular Data GenerationDifferentially Private Synthetic Data via APIs 4: Tabular Data
This paper investigates the problem of generating synthetic tabular data with differential privacy (DP) guarantees, enabling data sharing in sensitive domains. Despite extensive study, state-of-the-art methods often focu…
Tabular Data GenerationHierarchical Synthetic Tabular Data Generation: A Hybrid Top-Down and Bottom-Up Framework
Existing approaches for synthetic tabular data generation are based on either purely generative models or LLMs, both of which struggle with data heterogeneity, logical consistency, rare-event coverage, and robustness in …
Synthetic Data GenerationTabular Data GenerationPATE-TabTransGAN: Differentially Private Synthetic Tabular Data Generation via Transformer-Based Student Discrimination
Generating high-fidelity synthetic tabular data under formal differential privacy guarantees remains an open challenge. Methods that provide strong theoretical protection typically sacrifice the modeling of inter-feature…
Tabular Data GenerationGenerating Logically Consistent Synthetic Supply Chain Data with LLM-Driven Knowledge Graph Reasoning
Synthetic data offers a promising solution to two persistent barriers in supply chain analytics: data scarcity and data privacy. However, for synthetic data to support operational simulation and decision-making, it must …
Tabular Data GenerationTabKDE: Simple and Scalable Tabular Data Generation with Kernel Density Estimates
Tabular data generation considers a large table with multiple columns -- each column comprised of numerical, categorical, or sometimes ordinal values. The goal is to produce new rows for the table that replicate the dist…
Tabular Data GenerationBreaking the Quality-Privacy Tradeoff in Tabular Data Generation via In-Context Learning
Tabular data synthesis aims to generate high-quality data while preserving privacy. However, we find that existing tabular generative models exhibit a clear tradeoff in the small-data regime: improving data quality typic…
Tabular Data GenerationData AugmentationSAGE: Sparse Adaptive Guidance for Dependency-Aware Tabular Data Generation
Generating high-fidelity synthetic tabular data remains a critical challenge for enhancing data availability in privacy-sensitive and low-resource domains. Recent approaches leverage LLMs by representing table rows as se…
Tabular Data GenerationA Systematic Framework for Tabular Data Disentanglement
Tabular data, widely used in various applications such as industrial control systems, finance, and supply chain, often contains complex interrelationships among its attributes. Data disentanglement seeks to transform suc…
Tabular Data GenerationKnowledge-Guided Retrieval-Augmented Generation for Zero-Shot Psychiatric Data: Privacy Preserving Synthetic Data Generation
AI systems in healthcare research have shown potential to increase patient throughput and assist clinicians, yet progress is constrained by limited access to real patient data. To address this issue, we present a zero-sh…
Synthetic Data GenerationTabular Data GenerationClinical KnowledgeA Sobering Look at Tabular Data Generation via Probabilistic Circuits
Tabular data is more challenging to generate than text and images, due to its heterogeneous features and much lower sample sizes. On this task, diffusion-based models are the current state-of-the-art (SotA) model class, …
Tabular Data GenerationEmDT: Embedding Diffusion Transformer for Tabular Data Generation in Fraud Detection
Imbalanced datasets pose a difficulty in fraud detection, as classifiers are often biased toward the majority class and perform poorly on rare fraudulent transactions. Synthetic data generation is therefore commonly used…
Synthetic Data GenerationTabular Data GenerationFraud DetectionImproving TabPFN's Synthetic Data Generation by Integrating Causal Structure
Synthetic tabular data generation addresses data scarcity and privacy constraints in a variety of domains. Tabular Prior-Data Fitted Network (TabPFN), a recent foundation model for tabular data, has been shown capable of…
Synthetic Data GenerationTabular Data GenerationTabDLM: Free-Form Tabular Data Generation via Joint Numerical-Language Diffusion
Synthetic tabular data generation has attracted growing attention due to its importance for data augmentation, foundation models, and privacy. However, real-world tabular datasets increasingly contain free-form text fiel…
Tabular Data GenerationData AugmentationTeam, Then Trim: An Assembly-Line LLM Framework for High-Quality Tabular Data Generation
While tabular data is fundamental to many real-world machine learning (ML) applications, acquiring high-quality tabular data is usually labor-intensive and expensive. Limited by the scarcity of observations, tabular data…
Tabular Data GenerationCTTVAE: Latent Space Structuring for Conditional Tabular Data Generation on Imbalanced Datasets
Generating synthetic tabular data under severe class imbalance is essential for domains where rare but high-impact events drive decision-making. However, most generative models either overlook minority groups or fail to …
Tabular Data GenerationFraud DetectionExploring Transformer Placement in Variational Autoencoders for Tabular Data Generation
Tabular data remains a challenging domain for generative models. In particular, the standard Variational Autoencoder (VAE) architecture, typically composed of multilayer perceptrons, struggles to model relationships betw…
Tabular Data Generation