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
TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic DataCode4
Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted TreesCode4
TabDiff: a Multi-Modal Diffusion Model for Tabular Data GenerationCode2
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML EvaluationCode2
Language Models are Realistic Tabular Data GeneratorsCode2
dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data GenerationCode1
A Comprehensive Survey of Synthetic Tabular Data GenerationCode1
Diffusion Transformers for Tabular Data Time Series GenerationCode1
Scaling Up Diffusion and Flow-based XGBoost ModelsCode1
Unmasking Trees for Tabular DataCode1
TabPFGen -- Tabular Data Generation with TabPFNCode1
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
Continuous Diffusion for Mixed-Type Tabular DataCode1
TabuLa: Harnessing Language Models for Tabular Data SynthesisCode1
Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent SpaceCode1
FinDiff: Diffusion Models for Financial Tabular Data GenerationCode1
Generative Table Pre-training Empowers Models for Tabular PredictionCode1
DATGAN: Integrating expert knowledge into deep learning for synthetic tabular dataCode1
TabFairGAN: Fair Tabular Data Generation with Generative Adversarial NetworksCode1
Modeling Tabular data using Conditional GANCode1
CausalDiffTab: Mixed-Type Causal-Aware Diffusion for Tabular Data GenerationCode0
The Prompt is Mightier than the Example—0
Graph Conditional Flow Matching for Relational Data GenerationCode0
A Note on Statistically Accurate Tabular Data Generation Using Large Language ModelsCode0
TabRep: a Simple and Effective Continuous Representation for Training Tabular Diffusion ModelsCode0
Assessing Generative Models for Structured Data—0
GReaTER: Generate Realistic Tabular data after data Enhancement and Reduction—0
A Survey on Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, and Beyond—0
LLM-TabFlow: Synthetic Tabular Data Generation with Inter-column Logical Relationship PreservationCode0
Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraintsCode0
TabGen-ICL: Residual-Aware In-Context Example Selection for Tabular Data GenerationCode0
Generative adversarial networks vs large language models: a comparative study on synthetic tabular data generationCode0
TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer—0
Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation—0
Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap ClassCode0
Understanding and Mitigating Memorization in Diffusion Models for Tabular Data—0
Tabular data generation with tensor contraction layers and transformersCode0
DP-2Stage: Adapting Language Models as Differentially Private Tabular Data GeneratorsCode0
Towards a framework on tabular synthetic data generation: a minimalist approach: theory, use cases, and limitations—0
Generating Realistic Tabular Data with Large Language Models—0
Generating Tabular Data Using Heterogeneous Sequential Feature Forest Flow MatchingCode0
TAEGAN: Generating Synthetic Tabular Data For Data Augmentation—0
Preserving logical and functional dependencies in synthetic tabular dataCode0
Tabular Data Generation using Binary DiffusionCode0
Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative StudyCode0
On The Role of Prompt Construction In Enhancing Efficacy and Efficiency of LLM-Based Tabular Data Generation—0
HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection—0
High-Quality Tabular Data Generation using Post-Selected VAE—0
Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce ScenariosCode0
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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