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
CausalDiffTab: Mixed-Type Causal-Aware Diffusion for Tabular Data GenerationCode0
dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data GenerationCode1
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
A Comprehensive Survey of Synthetic Tabular Data GenerationCode1
Diffusion Transformers for Tabular Data Time Series GenerationCode1
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
TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic DataCode4
TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer—0
Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap ClassCode0
Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation—0
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
TabDiff: a Multi-Modal Diffusion Model for Tabular Data GenerationCode2
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
Scaling Up Diffusion and Flow-based XGBoost ModelsCode1
HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection—0
High-Quality Tabular Data Generation using Post-Selected VAE—0
Unmasking Trees for Tabular DataCode1
Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce ScenariosCode0
Are LLMs Naturally Good at Synthetic Tabular Data Generation?Code0
Under the Hood of Tabular Data Generation Models: Benchmarks with Extensive Tuning—0
TabPFGen -- Tabular Data Generation with TabPFNCode1
CTSyn: A Foundational Model for Cross Tabular Data Generation—0
Differentially Private Tabular Data Synthesis using Large Language Models—0
EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language ModelsCode1
Balanced Mixed-Type Tabular Data Synthesis with Diffusion ModelsCode0
PiShield: A PyTorch Package for Learning with Requirements—0
Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning AnalyticsCode0
FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data GenerationCode1
A self-attention-based differentially private tabular GAN with high data utility—0
Continuous Diffusion for Mixed-Type Tabular DataCode1
MMM and MMMSynth: Clustering of heterogeneous tabular data, and synthetic data generationCode0
Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive BenchmarkCode0
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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