{"total":90,"rows":[{"id":"2302.04062","title":"Machine Learning for Synthetic Data Generation: A Review","created":"2023-02-08","cited_by_count":120,"area":null},{"id":"2301.07573","title":"Synthcity: facilitating innovative use cases of synthetic data in different data modalities","created":"2023-01-18","cited_by_count":76,"area":null},{"id":"2205.03257","title":"Synthetic Data -- what, why and how?","created":"2022-05-06","cited_by_count":67,"area":null},{"id":"2310.16524","title":"Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data","created":"2023-10-25","cited_by_count":32,"area":null},{"id":"2304.03722","title":"Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data","created":"2023-04-07","cited_by_count":29,"area":null},{"id":"2305.09235","title":"Synthetic data, real errors: how (not) to publish and use synthetic data","created":"2023-05-16","cited_by_count":29,"area":null},{"id":"2402.04823","title":"How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data","created":"2024-02-07","cited_by_count":29,"area":null},{"id":"2404.15821","title":"SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Data","created":"2024-04-24","cited_by_count":27,"area":null},{"id":"2310.16981","title":"Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark","created":"2023-10-25","cited_by_count":23,"area":null},{"id":"2303.02186","title":"Causal Deep Learning","created":"2023-03-03","cited_by_count":20,"area":null},{"id":"2502.18237","title":"Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraints","created":"2025-02-25","cited_by_count":18,"area":null},{"id":"2302.12749","title":"SurvivalGAN: Generating Time-to-Event Data for Survival Analysis","created":"2023-02-24","cited_by_count":17,"area":null},{"id":"2201.08186","title":"Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations","created":"2022-01-20","cited_by_count":14,"area":null},{"id":"2211.05764","title":"DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems","created":"2022-11-09","cited_by_count":14,"area":null},{"id":"2301.13098","title":"CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy","created":"2023-01-30","cited_by_count":13,"area":null},{"id":"2304.10819","title":"Auditing and Generating Synthetic Data with Controllable Trust Trade-offs","created":"2023-04-21","cited_by_count":13,"area":null},{"id":"2307.03577","title":"CuTS: Customizable Tabular Synthetic Data Generation","created":"2023-07-07","cited_by_count":13,"area":null},{"id":"2403.17333","title":"The Pursuit of Fairness in Artificial Intelligence Models: A Survey","created":"2024-03-26","cited_by_count":13,"area":null},{"id":"2504.14657","title":"A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs","created":"2025-04-20","cited_by_count":13,"area":null},{"id":"2306.15636","title":"On the Usefulness of Synthetic Tabular Data Generation","created":"2023-06-27","cited_by_count":12,"area":null},{"id":"2505.22450","title":"Position: All Current Generative Fidelity and Diversity Metrics are Flawed","created":"2025-05-28","cited_by_count":12,"area":null},{"id":"2307.16526","title":"No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imaging","created":"2023-07-31","cited_by_count":11,"area":null},{"id":"2301.12351","title":"Emerging Synergies in Causality and Deep Generative Models: A Survey","created":"2023-01-29","cited_by_count":10,"area":null},{"id":"2401.01426","title":"Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference","created":"2024-01-02","cited_by_count":9,"area":null},{"id":"2304.03243","title":"Synthetic Data in Healthcare","created":"2023-04-06","cited_by_count":8,"area":null},{"id":"2405.05025","title":"Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges","created":"2024-05-08","cited_by_count":8,"area":null},{"id":"2303.02318","title":"Achieving Counterfactual Fairness for Anomaly Detection","created":"2023-03-04","cited_by_count":7,"area":null},{"id":"2309.17337","title":"Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools","created":"2023-09-29","cited_by_count":7,"area":null},{"id":"2209.06850","title":"CAT: Controllable Attribute Translation for Fair Facial Attribute Classification","created":"2022-09-14","cited_by_count":6,"area":null},{"id":"2311.03000","title":"Strong statistical parity through fair synthetic data","created":"2023-11-06","cited_by_count":6,"area":null},{"id":"2406.07029","title":"Fairness-Aware Meta-Learning via Nash Bargaining","created":"2024-06-11","cited_by_count":6,"area":null},{"id":"2212.02090","title":"Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling","created":"2022-12-05","cited_by_count":5,"area":null},{"id":"2403.08277","title":"VIGFace: Virtual Identity Generation for Privacy-Free Face Recognition","created":"2024-03-13","cited_by_count":5,"area":null},{"id":"2501.09591","title":"Metrics for Inter-Dataset Similarity with Example Applications in Synthetic Data and Feature Selection Evaluation -- Extended Version","created":"2025-01-16","cited_by_count":5,"area":null},{"id":"2510.26076","title":"New Money: A Systematic Review of Synthetic Data Generation for Finance","created":"2025-10-30","cited_by_count":5,"area":null},{"id":"2310.17687","title":"Consistent End-to-End Estimation for Counterfactual Fairness","created":"2023-10-26","cited_by_count":4,"area":null},{"id":"2404.13634","title":"Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networks","created":"2024-04-21","cited_by_count":4,"area":null},{"id":"2407.05593","title":"Unmasking Trees for Tabular Data","created":"2024-07-08","cited_by_count":4,"area":null},{"id":"2501.01785","title":"Can Synthetic Data be Fair and Private? A Comparative Study of Synthetic Data Generation and Fairness Algorithms","created":"2025-01-03","cited_by_count":4,"area":null},{"id":"2207.01575","title":"De-Biasing Generative Models using Counterfactual Methods","created":"2022-07-04","cited_by_count":3,"area":null},{"id":"2303.15592","title":"Uncovering Bias in Personal Informatics","created":"2023-03-27","cited_by_count":3,"area":null},{"id":"2411.03936","title":"GUIDE-VAE: Advancing Data Generation with User Information and Pattern Dictionaries","created":"2024-11-06","cited_by_count":3,"area":null},{"id":"2603.05396","title":"Harnessing Synthetic Data from Generative AI for Statistical Inference","created":"2026-03-05","cited_by_count":3,"area":null},{"id":"2302.11737","title":"Causally Disentangled Generative Variational AutoEncoder","created":"2023-02-23","cited_by_count":2,"area":null},{"id":"2305.02474","title":"MLHOps: Machine Learning for Healthcare Operations","created":"2023-05-04","cited_by_count":2,"area":null},{"id":"2311.05866","title":"Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes","created":"2023-11-10","cited_by_count":2,"area":null},{"id":"2402.03985","title":"A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets","created":"2024-02-06","cited_by_count":2,"area":null},{"id":"2406.14281","title":"FairX: A comprehensive benchmarking tool for model analysis using fairness, utility, and explainability","created":"2024-06-20","cited_by_count":2,"area":null},{"id":"2210.04369","title":"A Differentiable Distance Approximation for Fairer Image Classification","created":"2022-10-09","cited_by_count":1,"area":null},{"id":"2210.11275","title":"Causal Structural Hypothesis Testing and Data Generation Models","created":"2022-10-20","cited_by_count":1,"area":null},{"id":"2212.00911","title":"Navigating causal deep learning","created":"2022-12-01","cited_by_count":1,"area":null},{"id":"2301.01109","title":"On the causality-preservation capabilities of generative modelling","created":"2023-01-03","cited_by_count":1,"area":null},{"id":"2402.11710","title":"A Note on Bias to Complete","created":"2024-02-18","cited_by_count":1,"area":null},{"id":"2408.10755","title":"Generating Synthetic Fair Syntax-agnostic Data by Learning and Distilling Fair Representation","created":"2024-08-20","cited_by_count":1,"area":null},{"id":"2409.12642","title":"Deep generative models as an adversarial attack strategy for tabular machine learning","created":"2024-09-19","cited_by_count":1,"area":null},{"id":"2411.13251","title":"BelHouse3D: A Benchmark Dataset for Assessing Occlusion Robustness in 3D Point Cloud Semantic Segmentation","created":"2024-11-20","cited_by_count":1,"area":null},{"id":"2503.02968","title":"Privacy-Preserving Fair Synthetic Tabular Data","created":"2025-03-04","cited_by_count":1,"area":null},{"id":"2505.21160","title":"STEB: In Search of the Best Evaluation Approach for Synthetic Time Series","created":"2025-05-27","cited_by_count":1,"area":null},{"id":"2508.00718","title":"Democratizing Tabular Data Access with an Open$\\unicode{x2013}$Source Synthetic$\\unicode{x2013}$Data SDK","created":"2025-08-01","cited_by_count":1,"area":null},{"id":"2508.11810","title":"FairTabGen: High-Fidelity and Fair Synthetic Health Data Generation from Limited Samples","created":"2025-08-15","cited_by_count":1,"area":null},{"id":"2206.06354","title":"Differentiable and Transportable Structure Learning","created":"2022-06-13","cited_by_count":0,"area":null},{"id":"2303.01954","title":"Synthetic Data Generator for Adaptive Interventions in Global Health","created":"2023-03-03","cited_by_count":0,"area":null},{"id":"2303.03462","title":"Towards Composable Distributions of Latent Space Augmentations","created":"2023-03-06","cited_by_count":0,"area":null},{"id":"2305.12157","title":"(Machine) Learning to Be Like Thee? For Algorithm Education, Not Training","created":"2023-05-20","cited_by_count":0,"area":null},{"id":"2306.17828","title":"On the Cause of Unfairness: A Training Sample Perspective","created":"2023-06-30","cited_by_count":0,"area":null},{"id":"2405.07196","title":"Permissioned Blockchain-based Framework for Ranking Synthetic Data Generators","created":"2024-05-12","cited_by_count":0,"area":null},{"id":"2410.01423","title":"Fair4Free: Generating High-fidelity Fair Synthetic Samples using Data Free Distillation","created":"2024-10-02","cited_by_count":0,"area":null},{"id":"2412.20298","title":"An Experimental Study on Fairness-aware Machine Learning for Credit Scoring Problems","created":"2024-12-28","cited_by_count":0,"area":null},{"id":"2502.06658","title":"Guided Data Generation for Understanding Model Behavior","created":"2025-02-10","cited_by_count":0,"area":null},{"id":"2502.11031","title":"A Critical Review of Predominant Bias in Neural Networks","created":"2025-02-16","cited_by_count":0,"area":null},{"id":"2503.22569","title":"Comparing Methods for Bias Mitigation in Graph Neural Networks","created":"2025-03-28","cited_by_count":0,"area":null},{"id":"2504.18544","title":"Critical Challenges and Guidelines in Evaluating Synthetic Tabular Data: A Systematic Review","created":"2025-04-10","cited_by_count":0,"area":null},{"id":"2507.18561","title":"Beyond Internal Data: Constructing Complete Datasets for Fairness Testing","created":"2025-07-24","cited_by_count":0,"area":null},{"id":"2507.19700","title":"Disjoint Generation of Synthetic Data","created":"2025-07-25","cited_by_count":0,"area":null},{"id":"2508.02601","title":"StructSynth: Dependency Graphs as Generation Plans for Low-Data Tabular Synthesis with Language Models","created":"2025-08-04","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":"2510.09669","title":"Population synthesis with geographic coordinates","created":"2025-10-08","cited_by_count":0,"area":null},{"id":"2511.00964","title":"Using Synthetic Data to estimate the True Error is theoretically and practically doable","created":"2025-11-02","cited_by_count":0,"area":null},{"id":"2511.16204","title":"Causal Synthetic Data Generation in Recruitment","created":"2025-11-20","cited_by_count":0,"area":null},{"id":"2603.03748","title":"JANUS: Structured Bidirectional Generation for Guaranteed Constraints and Analytical Uncertainty","created":"2026-03-04","cited_by_count":0,"area":null},{"id":"2603.05327","title":"FairFinGAN: Fairness-aware Synthetic Financial Data Generation","created":"2026-03-05","cited_by_count":0,"area":null},{"id":"2603.20025","title":"Graph-Informed Adversarial Modeling: Infimal Subadditivity of Interpolative Divergences","created":"2026-03-20","cited_by_count":0,"area":null},{"id":"2603.20184","title":"Kolmogorov-Arnold causal generative models","created":"2026-03-20","cited_by_count":0,"area":null},{"id":"2603.26456","title":"Fair Data Pre-Processing with Imperfect Attribute Space","created":"2026-03-27","cited_by_count":0,"area":null},{"id":"2604.04290","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","created":"2026-04-05","cited_by_count":0,"area":null},{"id":"2605.05882","title":"Tuning Derivatives for Causal Fairness in Machine Learning","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2606.00503","title":"TabChange: Precise Attribute Changes in Tabular Data","created":"2026-05-30","cited_by_count":0,"area":null},{"id":"2606.13105","title":"Disparate Impact in Synthetic Data Generation","created":"2026-06-11","cited_by_count":0,"area":null},{"id":"2607.28945","title":"FairDiffuseVQVAE: Sampling-Time Fairness in Tabular Diffusion via Conditional Refinement of Vector-Quantized Latents","created":"2026-07-31","cited_by_count":0,"area":null},{"id":"2609.03180","title":"Portable Causal Fairness Across Synthetic Data Generator Families","created":"2026-09-02","cited_by_count":0,"area":null}],"next_cursor":null}