SOTAVerified

Tabular Data Generation

Generation of the tabular data using generative models

Papers

Showing 150 of 73 papers

TitleStatusHype
Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted TreesCode4
TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic DataCode4
Language Models are Realistic Tabular Data GeneratorsCode2
TabDiff: a Multi-Modal Diffusion Model for Tabular Data GenerationCode2
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML EvaluationCode2
Generative Table Pre-training Empowers Models for Tabular PredictionCode1
TabFairGAN: Fair Tabular Data Generation with Generative Adversarial NetworksCode1
Continuous Diffusion for Mixed-Type Tabular DataCode1
dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data GenerationCode1
Diffusion Transformers for Tabular Data Time Series GenerationCode1
Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent SpaceCode1
Modeling Tabular data using Conditional GANCode1
EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language ModelsCode1
DATGAN: Integrating expert knowledge into deep learning for synthetic tabular dataCode1
FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data GenerationCode1
FinDiff: Diffusion Models for Financial Tabular Data GenerationCode1
Unmasking Trees for Tabular DataCode1
TabuLa: Harnessing Language Models for Tabular Data SynthesisCode1
Scaling Up Diffusion and Flow-based XGBoost ModelsCode1
TabPFGen -- Tabular Data Generation with TabPFNCode1
A Comprehensive Survey of Synthetic Tabular Data GenerationCode1
ConvGeN: Convex space learning improves deep-generative oversampling for tabular imbalanced classification on smaller datasetsCode0
MMM and MMMSynth: Clustering of heterogeneous tabular data, and synthetic data generationCode0
Are LLMs Naturally Good at Synthetic Tabular Data Generation?Code0
Balanced Mixed-Type Tabular Data Synthesis with Diffusion ModelsCode0
CuTS: Customizable Tabular Synthetic Data GenerationCode0
Preserving logical and functional dependencies in synthetic tabular dataCode0
Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive BenchmarkCode0
Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraintsCode0
Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning AnalyticsCode0
Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap ClassCode0
Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative StudyCode0
TabGen-ICL: Residual-Aware In-Context Example Selection for Tabular Data GenerationCode0
TabRep: a Simple and Effective Continuous Representation for Training Tabular Diffusion ModelsCode0
TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular DataCode0
Tabular Data Generation using Binary DiffusionCode0
Tabular data generation with tensor contraction layers and transformersCode0
Tabular GANs for uneven distributionCode0
DP-2Stage: Adapting Language Models as Differentially Private Tabular Data GeneratorsCode0
CausalDiffTab: Mixed-Type Causal-Aware Diffusion for Tabular Data GenerationCode0
FLAIM: AIM-based Synthetic Data Generation in the Federated SettingCode0
Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce ScenariosCode0
Generating Tabular Data Using Heterogeneous Sequential Feature Forest Flow MatchingCode0
Generative adversarial networks vs large language models: a comparative study on synthetic tabular data generationCode0
Graph Conditional Flow Matching for Relational Data GenerationCode0
A Note on Statistically Accurate Tabular Data Generation Using Large Language ModelsCode0
LLM-TabFlow: Synthetic Tabular Data Generation with Inter-column Logical Relationship PreservationCode0
PiShield: A PyTorch Package for Learning with Requirements0
A self-attention-based differentially private tabular GAN with high data utility0
Assessing Generative Models for Structured Data0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Binary DiffusionDT Accuracy85.27Unverified
2GReaTDT Accuracy84.81Unverified
3Distill-GReaTDT Accuracy84.49Unverified
4TVAEDT Accuracy82.8Unverified
5CTGANDT Accuracy81.32Unverified
6CopulaGANDT Accuracy76.29Unverified
#ModelMetricClaimedVerifiedStatus
1GReaTParameters(M)355Unverified
2Distill-GReaTParameters(M)82Unverified
3Binary DiffusionParameters(M)1.5Unverified
4CopulaGANParameters(M)0.2Unverified
5CTGANParameters(M)0.2Unverified
6TVAEParameters(M)0.05Unverified
#ModelMetricClaimedVerifiedStatus
1Binary DiffusionDT Accuracy0.57Unverified
2GReaTDT Accuracy0.55Unverified
3Distill-GReaTDT Accuracy0.54Unverified
4TVAEDT Accuracy0.53Unverified
5CTGANDT Accuracy0.5Unverified
6CopulaGANDT Accuracy0.39Unverified
#ModelMetricClaimedVerifiedStatus
1Distill-GReaTDT Accuracy81.4Unverified
2GReaTDT Accuracy79.1Unverified
3TVAEDT Accuracy76.39Unverified
4Binary DiffusionDT Accuracy70.25Unverified
5CTGANDT Accuracy61.34Unverified
6CopulaGANDT Accuracy42.36Unverified
#ModelMetricClaimedVerifiedStatus
1GReaTDT Accuracy97.72Unverified
2Binary DiffusionDT Accuracy97.07Unverified
3Distill-GReaTDT Accuracy95.39Unverified
4TVAEDT Accuracy95.39Unverified
5CopulaGANDT Accuracy93.77Unverified
6CTGANDT Accuracy92.05Unverified
#ModelMetricClaimedVerifiedStatus
1Binary DiffusionDT Accuracy88.9Unverified
2GReaTDT Accuracy83.56Unverified
3TVAEDT Accuracy81.68Unverified
4Distill-GReaTDT Accuracy77.38Unverified
5CopulaGANDT Accuracy73.61Unverified
6CTGANDT Accuracy73.3Unverified