Paper creates fair synthetic data ensuring equal predictions across sensitive attributes.
problem Ensuring fair predictions across sensitive attributes in synthetic data.
method Equalizing target probability distributions across sensitive attributes in synthetic data generation.
result Synthetic data provides strong fair predictions, equal across all thresholds.
Synthetic data enhances analytics but requires careful volume management.
problem Accuracy of statistical methods on synthetic data vs. raw data.
method Synthetic Data Generation for Analytics framework using tabular diffusion models.
result Error rate decreases with more synthetic data but may stabilize or increase.
Paper proposes a new method to assess synthetic data generators.
problem Assessing the quality of implicit generative models.
method Kernelised Stein Statistic (KSD) test based on non-parametric Stein operator.
result Improved power performance compared to existing approaches.
SMOTE-DP enhances synthetic data privacy without sacrificing utility.
problem Balancing privacy and utility in synthetic data generation.
method Integrating SMOTE with differential privacy mechanisms.
result SMOTE-DP produces synthetic data that is both private and useful.
Paper establishes utility theory for synthetic data generation.
problem Lack of theoretical understanding in synthetic data utility.
method Statistical learning framework with two utility metrics: generalization and model ranking.
result Theoretical bounds for synthetic data utility metrics ensure comparable generalization and consistent model comparison.
CoFinDiff generates synthetic financial data capturing stylized facts and meeting specified conditions.
problem Limited data availability and difficulty in controlling synthetic financial data generation.
method Conditional diffusion model with cross-attention to incorporate conditions derived from price data.
result Synthetic data generated by CoFinDiff accurately meets specified conditions for trends and volatility.
A new method combines synthetic data analysis and DP generation to produce accurate uncertainty estimates.
problem Invalid inferences from DP synthetic data analysis.
method Combining synthetic data analysis techniques from MI and NA Bayesian modeling with a novel noise-aware synthetic data generation algorithm.
result Accurate confidence intervals from DP synthetic data are produced, wider with tighter privacy.
New algorithms for clustering and synthetic data generation of heterogeneous tabular datasets.
problem Clustering and generating synthetic data from heterogeneous tabular datasets with hidden cluster structure.
method Developed MMM and MMMsynth algorithms for clustering and synthetic data generation.
result MMMsynth algorithm outperforms other literature tabular-data generators and approaches real data performance.
Paper reviews synthetic data from AI models for statistical inference.
problem When can synthetic data be used reliably in statistical inference?
method Survey of generative models, statistical analysis of pitfalls.
result Principled use of synthetic data requires careful model specification.
Synthetic tabular data improves privacy while maintaining model performance.
problem Protecting privacy in synthetic data generation for machine learning.
method Deep generative models for tabular data, emphasizing privacy and model performance.
result Deep generative models enhance synthetic data generation for tabular datasets.
FedSyn generates synthetic data from multiple organizations' datasets.
problem Generating diverse synthetic data from limited datasets.
method Federated learning and GAN for privacy-preserving synthetic data generation.
result Synthetic data can be generated from diverse datasets without accessing individual data.
Synthetic data can amplify privacy in linear regression models.
problem Understanding how synthetic data can enhance privacy in linear regression models.
method Investigated through the linear regression framework, analyzing synthetic data generated from random inputs and controlled inputs.
result Releasing a limited number of synthetic data points amplifies privacy beyond the model's inherent guarantees when inputs are random, but not when inputs are controlled by an adversary.
Performing supervised learning from the data synthesized by using Generative Adversarial Networks (GANs), dubbed GAN-synthetic data, has two important applications. First, GANs may generate more labeled training data, which may help improve classification accuracy. Second, in scenarios where real data cannot be release…
Enhances statistical inference using synthetic data.
problem Limited labeled data for statistical inference.
method GESPI framework that combines synthetic and real data.
result Error rate remains below a user-specified bound and decreases with synthetic data quality.
AI techniques explain synthetic tabular data weaknesses.
problem Challenges in evaluating synthetic tabular data quality.
method Apply explainable AI to a binary detection classifier.
result Reveals inconsistencies, unrealistic dependencies, or missing patterns in synthetic data.
New methods reduce bias in synthetic data for machine learning.
problem Statistical bias in synthetic data generated for privacy.
method Re-weighting strategies using privatised likelihood ratios.
result Private importance weighting enhances synthetic data utility.
Proposes a new method for generating synthetic data using copula flows.
problem Challenges of current synthetic data generation methods, especially with mixed real and categorical variables.
method Uses normalizing flows to learn copula density and univariate marginals based on copula theory.
result Demonstrates improved synthetic data generation and density estimation.
DECAF generates fair synthetic data by embedding causal relationships.
problem Generating fair synthetic data from biased training data.
method DECAF uses a GAN with a structural causal model to embed causal relationships and debias synthetic data.
result DECAF successfully removes bias and generates high-quality synthetic data.
Framework uses synthetic data from pretrained models to improve predictive modeling.
problem Limited effectiveness of synthetic data from generative models for improving predictive performance.
method Proposes an end-to-end framework that generates and filters synthetic data through domain-specific statistical methods.
result Consistent improvements in predictive performance across various settings.
This paper uses LLMs to generate synthetic data to improve classification accuracy in imbalanced datasets.
problem Imbalanced classification and spurious correlation in data science.
method Develops novel theoretical foundations and uses transformer models to generate synthetic data.
result Transformer models can generate high-quality synthetic data to improve classification accuracy.
The paper presents a framework to quantify the trade-off between synthetic and real data.
problem Improving generalization with synthetic data when real data is scarce.
method Learning-theoretic framework leveraging algorithmic stability to derive generalization error bounds.
result Optimal synthetic-to-real data ratio minimizing expected test error as a function of Wasserstein distance.
Generative Adversarial Networks (GANs) have been used in many different applications to generate realistic synthetic data. We introduce a novel GAN with Autoencoder (GAN-AE) architecture to generate synthetic samples for variable length, multi-feature sequence datasets. In this model, we develop a GAN architecture with…
Bayesian approach for learning from synthetic data, improving model accuracy.
problem Lack of statistical properties and robust methods for learning from synthetic data.
method Bayesian paradigm to update model parameters considering synthetic data generating process and learning task.
result Novel approach outperforms standard methods in supervised learning and inference problems.
The paper addresses the selection of synthetic data for improving classifier performance, focusing on the role of covariance shift.
problem The effectiveness of synthetic data in improving classifier performance is questioned, and the specific properties affecting this performance are unclear.
method The paper uses high-dimensional regression to analyze synthetic data selection, focusing on the covariance shift between synthetic and target distributions.
result The covariance shift between synthetic and target distributions affects the generalization error of classifiers, but the mean shift does not.
Diffusion models generate private synthetic images with high quality.
problem Generating private synthetic images from sensitive datasets.
method Differentially private fine-tuning of pre-trained diffusion models.
result Improved synthetic data quality and accuracy compared to non-private models.
Synthetic augmentation helps but not always in imbalanced learning.
problem Imbalanced learning causes poor performance on rare classes.
method Developed a statistical framework for synthetic augmentation in imbalanced learning.
result Synthetic augmentation is not always beneficial and depends on the imbalance regime.
Develops a private synthetic graph generator using Gromov-Wasserstein distance.
problem Creating private synthetic networks for complex data.
method Random connection model, fused Gromov-Wasserstein distance, differential privacy.
result Effective algorithm for generating private synthetic graphs with theoretical guarantees.
This paper uses deep generative models to create synthetic financial data for portfolio and risk modeling.
problem Challenges in empirical research due to privacy, accessibility, and reproducibility issues in financial data.
method Investigates the use of Time-series Generative Adversarial Networks (TimeGAN) and Variational Autoencoders (VAEs) to generate synthetic financial return series.
result Synthetic data from TimeGAN closely mimics real financial data in distributional shapes, volatility, and autocorrelation.
Framework evaluates quality of synthetic data generated with differential privacy.
problem Ensuring synthetic data retains statistical quality after applying differential privacy.
method Developed a framework to evaluate synthetic data quality from a practical researcher's viewpoint.
result Synthetic data can be evaluated against training data or underlying populations, and for specific tasks like inference or prediction.
We study the sample complexity of private synthetic data generation over an unbounded sized class of statistical queries, and show that any class that is privately proper PAC learnable admits a private synthetic data generator (perhaps non-efficient). Previous work on synthetic data generators focused on the case that …
We draw a formal connection between using synthetic training data to optimize neural network parameters and approximate, Bayesian, model-based reasoning. In particular, training a neural network using synthetic data can be viewed as learning a proposal distribution generator for approximate inference in the synthetic-d…
PRRO generates synthetic tabular data that improves SL performance and class distribution.
problem Low SL utility of synthetic data due to class imbalance and overlooked data relationships.
method Data pruning and column reordering to optimize SL utility.
result Synthetic data generated with PRRO enhances predictive performance and class distribution.
New synthetic data analysis reveals high type 1 error rates.
problem Analyzing synthetic data for inference raises significant methodological challenges.
method Developed statistical inference tools and conducted a simulation study.
result Type 1 error rates are unacceptably high in synthetic data analysis.
Paper compares MICE-based methods to deep generative models for synthetic data in ratemaking.
problem Limited access to high-quality data for actuarial ratemaking.
method Benchmarked MICE-based models against VAEs and CTA-GANs.
result MICE-based models preserve marginal distributions and multivariate relationships better than deep models.
New strategy debiases synthetic data generated by DGMs for improved statistical inference.
problem Bias and imprecision in synthetic data generated by DGMs impede statistical convergence and inference.
method Debiasing strategy based on debiased and targeted machine learning.
result Enhanced convergence rates and accurate estimators with easily approximated variances.
New method generates synthetic survival data by conditioning on event times and censoring indicators.
problem Generating accurate synthetic survival data with censored event times.
method Conditioning covariates on event times and censoring indicators using existing tabular data generation models.
result Our method consistently outperforms baselines and improves survival model performance.
Enhanced synthetic dataset improves asset allocation analysis.
problem Lack of realistic synthetic data for fixed income portfolio construction.
method Improved CorrGAN model for synthetic correlation matrices and Encoder-Decoder model for additional data conditioning.
result Synthetic dataset enhances portfolio construction and asset allocation analysis.
SPI uses synthetic data to improve predictive inference efficiency.
problem Inefficient predictive inference with scarce calibration data.
method Integrates synthetic data to align nonconformity scores and improve coverage guarantees.
result SPI yields substantially tighter and more informative prediction sets.
We introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversarial Networks (GANs). This framework can be used to take in raw sensitive data and privately train a model for generating synthetic data that …
Efficient synthetic data generation improves model performance on tabular data.
problem Improving model robustness and performance with scarce or low-quality data.
method Hardness characterization to identify high-value training points, generating synthetic data only from these points.
result Synthetic data generated from hardest points outperforms non-targeted methods on tabular datasets.
Hybrid LLM generates synthetic data preserving causal parameters.
problem Synthetic data fails to accurately estimate causal effects.
method Combines model-based covariate synthesis with separately learned propensity and outcome models.
result Hybrid framework ensures causal structure in synthetic data.
Privacy is enhanced by synthetic data release even with unlimited data.
problem Improving privacy guarantees for synthetic data release.
method Analyzing a bounded-parameter assumption to show privacy amplification persists with unlimited synthetic records.
result Privacy amplification is possible even with an unbounded number of synthetic records.
Improves synthetic-to-real generalization without real data.
problem Synthetic models struggle with real data generalization.
method Encourages similar ImageNet representations and automates learning rates.
result Significant improvement in synthetic-to-real generalization.
DAISYnt evaluates synthetic data quality and privacy in regulated domains.
problem Balancing data quality and privacy in regulated domains.
method Developed a suite of advanced tests (DAISYnt) to evaluate synthetic data quality and privacy.
result DAISYnt sets a de facto standard for synthetic data evaluation in regulated domains.
Generative synthetic data can preserve predictive accuracy but distort causal inference.
problem Distortion of average treatment effect estimates in synthetic data.
method Hybrid synthetic-data framework that generates covariates while modeling treatment and outcome mechanisms separately.
result Hybrid synthesis improves causal fidelity compared to fully generative baselines.
New KD-tree based method for private synthetic data generation.
problem Creating private synthetic data that accurately represents real data.
method KD-trees combined with noise perturbation for differentially private synthetic data generation.
result Our data-dependent approach improves utility over prior work and scales well.
Method preserves correlations in synthetic data.
problem Preserving dependence structure of original data.
method Orthogonal Procrustes problem for restoring Pearson correlation.
result Restores Pearson correlation structure while preserving feature distributions and downstream tasks performance.
Paper proposes synthetic data generator to study and mitigate bias in machine learning.
problem Bias in machine learning data can lead to unfair outcomes.
method Developed a synthetic data generator to introduce and analyze various types of bias.
result Demonstrated how synthetic data can be used to study and mitigate bias in machine learning models.