{"total":62,"rows":[{"id":"2402.17944","title":"Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey","created":"2024-02-27","cited_by_count":159,"area":null},{"id":"2405.09591","title":"A Comprehensive Survey on Data Augmentation","created":"2024-05-15","cited_by_count":40,"area":null},{"id":"2504.16506","title":"A Comprehensive Survey of Synthetic Tabular Data Generation","created":"2025-04-23","cited_by_count":28,"area":"diffusion"},{"id":"2502.11420","title":"Training-Free Guidance Beyond Differentiability: Scalable Path Steering with Tree Search in Diffusion and Flow Models","created":"2025-02-17","cited_by_count":24,"area":null},{"id":"2405.20690","title":"DiffPuter: Empowering Diffusion Models for Missing Data Imputation","created":"2024-05-31","cited_by_count":19,"area":"diffusion"},{"id":"2312.10431","title":"Continuous Diffusion for Mixed-Type Tabular Data","created":"2023-12-16","cited_by_count":17,"area":"diffusion"},{"id":"2407.18013","title":"Self-Supervision Improves Diffusion Models for Tabular Data Imputation","created":"2024-07-25","cited_by_count":16,"area":"diffusion"},{"id":"2407.21523","title":"Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI","created":"2024-07-31","cited_by_count":15,"area":null},{"id":"2503.05954","title":"A Survey on Deep Learning Approaches for Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, Diversity, and Beyond","created":"2025-03-07","cited_by_count":15,"area":null},{"id":"2506.05940","title":"Exponential Family Variational Flow Matching for Tabular Data Generation","created":"2025-06-06","cited_by_count":15,"area":"diffusion"},{"id":"2501.12012","title":"TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data","created":"2025-01-21","cited_by_count":14,"area":null},{"id":"2406.08311","title":"Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Framework","created":"2024-06-12","cited_by_count":8,"area":null},{"id":"2412.15657","title":"Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap Class","created":"2024-12-20","cited_by_count":8,"area":null},{"id":"2502.17119","title":"Diffusion and Flow Matching Models for Tabular Data: A Survey","created":"2025-02-24","cited_by_count":8,"area":"diffusion"},{"id":"2402.03232","title":"Explicit Flow Matching: On The Theory of Flow Matching Algorithms with Applications","created":"2024-02-05","cited_by_count":6,"area":"diffusion"},{"id":"2411.03351","title":"Tabular Data Synthesis with Differential Privacy: A Survey","created":"2024-11-04","cited_by_count":6,"area":null},{"id":"2512.00698","title":"Flow Matching for Tabular Data Synthesis","created":"2025-11-30","cited_by_count":6,"area":"diffusion"},{"id":"2402.03819","title":"Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants","created":"2024-02-06","cited_by_count":5,"area":null},{"id":"2404.12314","title":"Guided Discrete Diffusion for Electronic Health Record Generation","created":"2024-04-18","cited_by_count":5,"area":"diffusion"},{"id":"2406.07658","title":"Treeffuser: Probabilistic Predictions via Conditional Diffusions with Gradient-Boosted Trees","created":"2024-06-11","cited_by_count":5,"area":"diffusion"},{"id":"2408.16046","title":"Scaling Up Diffusion and Flow-based XGBoost Models","created":"2024-08-28","cited_by_count":5,"area":null},{"id":"2504.04798","title":"TabRep: Training Tabular Diffusion Models with a Simple and Effective Continuous Representation","created":"2025-04-07","cited_by_count":5,"area":"diffusion"},{"id":"2506.09258","title":"CFMI: Flow Matching for Missing Data Imputation","created":"2025-06-10","cited_by_count":5,"area":"diffusion"},{"id":"2407.05593","title":"Unmasking Trees for Tabular Data","created":"2024-07-08","cited_by_count":4,"area":null},{"id":"2412.05153","title":"A text-to-tabular approach to generate synthetic patient data using LLMs","created":"2024-12-06","cited_by_count":4,"area":null},{"id":"2503.02152","title":"Tabby: A Language Model Architecture for Tabular and Structured Data Synthesis","created":"2025-03-04","cited_by_count":4,"area":"synth"},{"id":"2510.02625","title":"One Pipeline, Many Transformers: Pattern-Specific Imputation Specialists for Tabular Missing Data","created":"2025-10-03","cited_by_count":4,"area":null},{"id":"2601.22816","title":"Cascaded Flow Matching for Heterogeneous Tabular Data with Mixed-Type Features","created":"2026-01-30","cited_by_count":4,"area":"diffusion"},{"id":"2404.18219","title":"BUFF: Boosted Decision Tree based Ultra-Fast Flow matching","created":"2024-04-28","cited_by_count":3,"area":"diffusion"},{"id":"2405.09516","title":"Generalization Bounds for Causal Regression: Insights, Guarantees and Sensitivity Analysis","created":"2024-05-15","cited_by_count":3,"area":null},{"id":"2505.15668","title":"Graph-Conditional Flow Matching for Relational Data Generation","created":"2025-05-21","cited_by_count":3,"area":"diffusion"},{"id":"2405.16971","title":"A Unified Framework for Tabular Generative Modeling: Loss Functions, Benchmarks, and Improved Multi-objective Bayesian Optimization Approaches","created":"2024-05-27","cited_by_count":2,"area":null},{"id":"2406.01813","title":"Diffusion Boosted Trees","created":"2024-06-03","cited_by_count":2,"area":"diffusion"},{"id":"2501.01216","title":"TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer","created":"2025-01-02","cited_by_count":2,"area":null},{"id":"2501.17770","title":"Generative Unordered Flow for Set-Structured Data Generation","created":"2025-01-29","cited_by_count":2,"area":null},{"id":"2505.13213","title":"Diffusion Models with Double Guidance: Generate with aggregated datasets","created":"2025-05-19","cited_by_count":2,"area":"diffusion"},{"id":"2604.03779","title":"CountsDiff: A Diffusion Model on the Natural Numbers for Generation and Imputation of Count-Based Data","created":"2026-04-04","cited_by_count":2,"area":"diffusion"},{"id":"2605.01676","title":"Missingness-aware Data Imputation via AI-powered Bayesian Generative Modeling","created":"2026-05-03","cited_by_count":2,"area":null},{"id":"2408.02128","title":"Table Transformers for Imputing Textual Attributes","created":"2024-08-04","cited_by_count":1,"area":null},{"id":"2410.03535","title":"NRGBoost: Energy-Based Generative Boosted Trees","created":"2024-10-04","cited_by_count":1,"area":null},{"id":"2410.15516","title":"Generating Tabular Data Using Heterogeneous Sequential Feature Forest Flow Matching","created":"2024-10-20","cited_by_count":1,"area":"diffusion"},{"id":"2502.13450","title":"Interleaved Gibbs Diffusion: Generating Discrete-Continuous Data with Implicit Constraints","created":"2025-02-19","cited_by_count":1,"area":null},{"id":"2601.11444","title":"When Are Two Scores Better Than One? Investigating Ensembles of Diffusion Models","created":"2026-01-16","cited_by_count":1,"area":"diffusion"},{"id":"2606.03347","title":"AugMask: Training Diffusion Models on Incomplete Tabular Data via Stochastic Augmentation and Masking","created":"2026-06-02","cited_by_count":1,"area":"diffusion"},{"id":"2606.13240","title":"Towards More General Control of Diffusion Models Using Jeffrey Guidance","created":"2026-06-11","cited_by_count":1,"area":"diffusion"},{"id":"2506.08844","title":"IMAGIC-500: IMputation benchmark on A Generative Imaginary Country (500k samples)","created":"2025-06-10","cited_by_count":0,"area":null},{"id":"2508.06647","title":"Privacy-Preserving Tabular Synthetic Data Generation Using TabularARGN","created":"2025-08-08","cited_by_count":0,"area":null},{"id":"2511.01196","title":"An Interdisciplinary and Cross-Task Review on Missing Data Imputation","created":"2025-11-03","cited_by_count":0,"area":null},{"id":"2511.16571","title":"Boosting Predictive Performance on Tabular Data through Data Augmentation with Latent-Space Flow-Based Diffusion","created":"2025-11-20","cited_by_count":0,"area":"diffusion"},{"id":"2601.18500","title":"Nearly Optimal Bayesian Inference for Structural Missingness","created":"2026-01-26","cited_by_count":0,"area":null},{"id":"2601.20854","title":"Exploring Transformer Placement in Variational Autoencoders for Tabular Data Generation","created":"2026-01-28","cited_by_count":0,"area":null},{"id":"2603.17717","title":"Machine Learning for Network Attacks Classification and Statistical Evaluation of Adversarial Learning Methodologies for Synthetic Data Generation","created":"2026-03-18","cited_by_count":0,"area":null},{"id":"2604.05257","title":"Extending Tabular Denoising Diffusion Probabilistic Models for Time-Series Data Generation","created":"2026-04-06","cited_by_count":0,"area":"diffusion"},{"id":"2606.00934","title":"Efficient Synthetic Network Generation via Latent Embedding Reconstruction","created":"2026-05-31","cited_by_count":0,"area":null},{"id":"2606.05073","title":"Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2606.09257","title":"BSTabDiff: Block-Subunit Diffusion Priors for High-Dimensional Tabular Data Generation","created":"2026-06-08","cited_by_count":0,"area":"diffusion"},{"id":"2607.28698","title":"Flow Matching with Missing Data","created":"2026-07-30","cited_by_count":0,"area":"diffusion"},{"id":"2607.28864","title":"Conditioning Tree-Based Diffusions and Flows for Probabilistic Tabular Regression","created":"2026-07-30","cited_by_count":0,"area":"diffusion"},{"id":"2608.07294","title":"FUSE: Feature-Wise Unified Specialization with Cross-Column Exchange for Mixed-Type Tabular Flow Matching","created":"2026-08-07","cited_by_count":0,"area":"diffusion"},{"id":"2608.19801","title":"Unsupervised Anomaly Detection Using Flow Matching on Tabular Data","created":"2026-08-20","cited_by_count":0,"area":null},{"id":"2610.15699","title":"TabMosaic: Unified In-Context Diffusion for Tabular Generative Tasks","created":"2026-10-11","cited_by_count":0,"area":null},{"id":"2610.20342","title":"When Does Task-Aware Imputation Matter? An Analysis of Robustness Under Train-Test Mismatch","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}