Papers Tabular Data Generation
“Tabular Data Generation” 태그가 달린 논문 112편 · 필터 해제
dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation
We propose dpmm, an open-source library for synthetic data generation with Differentially Private (DP) guarantees. It includes three popular marginal models -- PrivBayes, MST, and AIM -- that achieve superior utility and…
Synthetic Data GenerationTabular Data GenerationThe Prompt is Mightier than the Example
Numerous recent prompt optimization approaches like chain-of-thought, have been demonstrated to significantly improve the quality of content generated by large language models (LLMs). In-context learning (ICL), a recent …
In-Context LearningSynthetic Data GenerationTabular Data GenerationGraph Conditional Flow Matching for Relational Data Generation
Data synthesis is gaining momentum as a privacy-enhancing technology. While single-table tabular data generation has seen considerable progress, current methods for multi-table data often lack the flexibility and express…
Graph Neural NetworkTabular Data GenerationA Note on Statistically Accurate Tabular Data Generation Using Large Language Models
Large language models (LLMs) have shown promise in synthetic tabular data generation, yet existing methods struggle to preserve complex feature dependencies, particularly among categorical variables. This work introduces…
Tabular Data GenerationA Comprehensive Survey of Synthetic Tabular Data Generation
Tabular data remains one of the most prevalent and critical data formats across diverse real-world applications. However, its effective use in machine learning (ML) is often constrained by challenges such as data scarcit…
Privacy PreservingSurveySynthetic Data GenerationTabular Data GenerationDiffusion Transformers for Tabular Data Time Series Generation
Tabular data generation has recently attracted a growing interest due to its different application scenarios. However, generating time series of tabular data, where each element of the series depends on the others, remai…
Tabular Data GenerationTime SeriesTime Series GenerationVideo GenerationTabRep: a Simple and Effective Continuous Representation for Training Tabular Diffusion Models
Diffusion models have been the predominant generative model for tabular data generation. However, they face the conundrum of modeling under a separate versus a unified data representation. The former encounters the chall…
Tabular Data GenerationAssessing Generative Models for Structured Data
Synthetic tabular data generation has emerged as a promising method to address limited data availability and privacy concerns. With the sharp increase in the performance of large language models in recent years, research…
Synthetic Data GenerationTabular Data GenerationGReaTER: Generate Realistic Tabular data after data Enhancement and Reduction
Tabular data synthesis involves not only multi-table synthesis but also generating multi-modal data (e.g., strings and categories), which enables diverse knowledge synthesis. However, separating numerical and categorical…
In-Context LearningTabular Data GenerationA Survey on Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, and Beyond
Generative modelling has become the standard approach for synthesising tabular data. However, different use cases demand synthetic data to comply with different requirements to be useful in practice. In this survey, we r…
NavigatePrivacy PreservingSurveyTabular Data GenerationLLM-TabFlow: Synthetic Tabular Data Generation with Inter-column Logical Relationship Preservation
Synthetic tabular data have widespread applications in industrial domains such as healthcare, finance, and supply chains, owing to their potential to protect privacy and mitigate data scarcity. However, generating realis…
Large Language ModelTabular Data GenerationBeyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraints
Synthetic tabular data generation has traditionally been a challenging problem due to the high complexity of the underlying distributions that characterise this type of data. Despite recent advances in deep generative mo…
Tabular Data GenerationTabGen-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generation
Large Language models (LLMs) have achieved encouraging results in tabular data generation. However, existing approaches require fine-tuning, which is computationally expensive. This paper explores an alternative: prompti…
In-Context LearningTabular Data GenerationGenerative adversarial networks vs large language models: a comparative study on synthetic tabular data generation
We propose a new framework for zero-shot generation of synthetic tabular data. Using the large language model (LLM) GPT-4o and plain-language prompting, we demonstrate the ability to generate high-fidelity tabular data w…
Generative Adversarial NetworkLanguage ModelingLanguage ModellingLarge Language Model+1TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data
Synthetic data generation for tabular datasets must balance fidelity, efficiency, and versatility to meet the demands of real-world applications. We introduce the Tabular Auto-Regressive Generative Network (TabularARGN),…
FairnessImputationSynthetic Data GenerationTabular Data GenerationTabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer
Transformers have shown impressive results in tabular data generation. However, they lack domain-specific inductive biases which are critical for preserving the intrinsic characteristics of tabular data. They also suffer…
BenchmarkingQuantizationTabular Data GenerationDifferentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation
The increasing demand for privacy-preserving data analytics in finance necessitates solutions for synthetic data generation that rigorously uphold privacy standards. We introduce DP-Fed-FinDiff framework, a novel integra…
DenoisingFederated LearningPrivacy PreservingSynthetic Data Generation+1Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap Class
Handling imbalance in class distribution when building a classifier over tabular data has been a problem of long-standing interest. One popular approach is augmenting the training dataset with synthetically generated dat…
imbalanced classificationTabular Data GenerationUnderstanding and Mitigating Memorization in Diffusion Models for Tabular Data
Tabular data generation has attracted significant research interest in recent years, with the tabular diffusion models greatly improving the quality of synthetic data. However, while memorization, where models inadverten…
Data AugmentationMemorizationTabular Data GenerationText GenerationTabular data generation with tensor contraction layers and transformers
Generative modeling for tabular data has recently gained significant attention in the Deep Learning domain. Its objective is to estimate the underlying distribution of the data. However, estimating the underlying distrib…
Density EstimationTabular Data Generation