SOTAVerified

Tabular Data Generation

Generation of the tabular data using generative models

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–50 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
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML EvaluationCode2
Language Models are Realistic Tabular Data GeneratorsCode2
TabDiff: a Multi-Modal Diffusion Model for Tabular Data GenerationCode2
FinDiff: Diffusion Models for Financial Tabular Data GenerationCode1
Modeling Tabular data using Conditional GANCode1
Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent SpaceCode1
A Comprehensive Survey of Synthetic Tabular Data GenerationCode1
Generative Table Pre-training Empowers Models for Tabular PredictionCode1
Unmasking Trees for Tabular DataCode1
Continuous Diffusion for Mixed-Type Tabular DataCode1
DATGAN: Integrating expert knowledge into deep learning for synthetic tabular dataCode1
TabuLa: Harnessing Language Models for Tabular Data SynthesisCode1
TabPFGen -- Tabular Data Generation with TabPFNCode1
Scaling Up Diffusion and Flow-based XGBoost ModelsCode1
Diffusion Transformers for Tabular Data Time Series GenerationCode1
dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data GenerationCode1
TabFairGAN: Fair Tabular Data Generation with Generative Adversarial NetworksCode1
EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language ModelsCode1
FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data GenerationCode1
PiShield: A PyTorch Package for Learning with Requirements—0
A self-attention-based differentially private tabular GAN with high data utility—0
Assessing Generative Models for Structured Data—0
A Survey on Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, and Beyond—0
Causal-TGAN: Causally-Aware Synthetic Tabular Data Generative Adversarial Network—0
Comparing Synthetic Tabular Data Generation Between a Probabilistic Model and a Deep Learning Model for Education Use Cases—0
Composable Generative Models—0
CTSyn: A Foundational Model for Cross Tabular Data Generation—0
Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation—0
Differentially Private Tabular Data Synthesis using Large Language Models—0
DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generation—0
Generating Realistic Tabular Data with Large Language Models—0
Generative Forests—0
GReaTER: Generate Realistic Tabular data after data Enhancement and Reduction—0
High-Quality Tabular Data Generation using Post-Selected VAE—0
MargCTGAN: A "Marginally'' Better CTGAN for the Low Sample Regime—0
On The Role of Prompt Construction In Enhancing Efficacy and Efficiency of LLM-Based Tabular Data Generation—0
On the Usefulness of Synthetic Tabular Data Generation—0
HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection—0
ResBit: Residual Bit Vector for Categorical Values—0
TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer—0
TAEGAN: Generating Synthetic Tabular Data For Data Augmentation—0
The Prompt is Mightier than the Example—0
Towards a framework on tabular synthetic data generation: a minimalist approach: theory, use cases, and limitations—0
Understanding and Mitigating Memorization in Diffusion Models for Tabular Data—0
Under the Hood of Tabular Data Generation Models: Benchmarks with Extensive Tuning—0
CuTS: Customizable Tabular Synthetic Data GenerationCode0
Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive BenchmarkCode0
A Note on Statistically Accurate Tabular Data Generation Using Large Language ModelsCode0
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All datasetsAdult Census IncomeCalifornia Housing PricesDiabetesHELOCSICKTravel

Benchmark Results

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