Researchers formalize PD and PFI to relate them to data generating process.
problem Lack of theory linking PD and PFI to data generating process.
method Formalize PD and PFI as estimators of ground truth estimands, account for model variance with learner-PD and learner-PFI.
result PD and PFI estimates deviate from ground truth due to statistical biases, model variance, and Monte Carlo approximation errors.
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.
Generative method avoids function estimation for data generation.
problem Challenges in function estimation for generative models.
method Deterministic point transport with gradient descent.
result Data generation possible without function estimation.
Multitask Gaussian process regression reduces data generation costs for molecular property prediction.
problem Data bottleneck in training surrogate models for molecular properties.
method Multitask Gaussian process regression over heterogeneous data sources (CC and DFT).
result Predicts at CC-level accuracy with over an order of magnitude reduction in data generation cost.
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.
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.
AutoSimulate efficiently optimizes synthetic data generation.
problem Optimizing synthetic data generation for machine learning.
method Differentiable approximation of the objective function for efficient optimization.
result Significantly faster (up to 50x) and more efficient (up to 30x) synthetic data generation.
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 …
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.
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.
ASADG improves data generation for accurate surrogate modeling of complex physical problems.
problem Training surrogate models on imbalanced data leads to inaccurate predictions.
method ASADG iteratively adds input data to improve the representation of the response manifold.
result ASADG generates more representative input data compared to LHS for better model accuracy.
The study addresses overlooked data-generating processes in time-series asset pricing.
problem The literature on time-series asset pricing overlooks the data-generating processes for factors expressed in return differences.
method The study proposes a new definition of returns and compound returns for factors, and uses OLS with net returns for single-index models.
result OLS with net returns for single-index models leads to inflated alphas, exaggerated t-values, and overestimated Sharpe ratios.
This work analyzes VQ-VAEs using information theory, focusing on latent variables and their impact on generalization and data generation.
problem Lack of theoretical analysis for latent variables in unsupervised models like VQ-VAEs.
method Information-theoretic analysis, introducing a novel data-dependent prior.
result Derives a generalization error bound for VQ-VAEs that depends on LV complexity and encoder, not decoder.
New VAE model improves data fitting without sacrificing computational efficiency.
problem Limitation of Gaussian assumption in VAE for continuous variable fitting.
method Infinite mixture of asymmetric Laplace distribution in decoder, nonparametric M-estimator for quantile estimation.
result Model demonstrates superior data privacy adjustment and better distribution fitting.
This study benchmarks tabular data generation models, optimizing hyperparameters and feature encodings.
problem Generating realistic tabular data is challenging due to heterogeneity, non-smooth distributions, and complex dependencies.
method Comprehensive evaluation of five model families on 16 datasets, considering hyperparameters, feature encodings, and architectures.
result Large-scale dataset-specific tuning significantly improves model performance, especially for diffusion-based models.
A new method improves data generation quality by correcting score mismatches.
problem Score mismatch issue in conditional score-based data generation methods.
method Denoising Likelihood Score Matching (DLSM) loss for classifier training.
result The proposed method outperforms previous methods on Cifar-10 and Cifar-100 benchmarks.
This paper improves prediction rule ensembles using model-based data generation.
problem Improving the sparsity and predictive accuracy of prediction rule ensembles.
method The authors use surrogate models to train Lasso regression with data generated by a boosted decision tree ensemble, improving PRE performance.
result The use of surrogacy models can substantially improve the sparsity of PRE while retaining predictive accuracy.
Recent advances in machine learning have largely benefited from the massive accessible training data. However, large-scale data sharing has raised great privacy concerns. In this work, we propose a novel privacy-preserving data Generative model based on the PATE framework (G-PATE), aiming to train a scalable differenti…
GUIDE-VAE generates user-guided data with improved realism and performance.
problem Generating data points for multi-user datasets while considering user information.
method Conditional generative model that integrates user embeddings and a pattern dictionary-based covariance composition.
result GUIDE-VAE outperforms conventional VAEs in multi-user settings, especially under data imbalance.
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.
CSDM integrates compressed sensing into diffusion models for faster data generation.
problem Efficiently generating synthetic data in high-dimensional spaces.
method Integrating compressed sensing into diffusion models (CSDM) to reduce dimensionality and accelerate inference.
result Achieves provably faster convergence and better latent space dimension selection.
Discrete diffusion models improve data generation for discrete data like language and graphs.
problem Adapting diffusion models to discrete state spaces for better data generation.
method Formulated as CTMCs, used uniformization of continuous Markov chains for sampling.
result Derive guarantees for sampling from any distribution on a hypercube, aligning with state-of-the-art achievements.
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.
A minimalist approach generates synthetic tabular data with sparse PCA and XGBoost.
problem Generating robust synthetic tabular data for model testing.
method Minimalistic unsupervised SparsePCA encoder with XGBoost decoder.
result The method provides an alternative to raw and quantile perturbation for model robustness testing.
Unified approach for multimodal data prediction using synthetic data generation.
problem Challenges in integrating heterogeneous data types for accurate predictive performance.
method Generative Distribution Prediction (GDP) framework that uses multimodal synthetic data generation.
result Empirical validation across four tasks demonstrates versatility and effectiveness of GDP.
We consider streaming principal component analysis when the stochastic data-generating model is subject to perturbations. While existing models assume a fixed covariance, we adopt a robust perspective where the covariance matrix belongs to a temporal uncertainty set. Under this setting, we provide fundamental limits on…
Hermite polynomials improve private data generation by reducing feature count.
problem Infinite-dimensional features in kernel mean embedding are impractical for private data generation.
method Replace random features with Hermite polynomial features, leveraging their ordered nature.
result Hermite polynomial features yield a more accurate approximation of kernel mean embedding with fewer features.
DFM model detects communities in weighted networks without distributional assumptions.
problem Challenges in community detection for weighted networks.
method Distribution-Free Model (DFM) for weighted networks, using spectral clustering.
result Spectral clustering yields consistent community detection under DFM.
This work introduces a new metric to assess the fidelity of surrogate models to the underlying data-generating signal.
problem The limitations of fidelity-based explanations in explainable AI.
method Introduces the linearity score λ(f) to quantify the extent of a regression network's linear decodability. result High-fidelity surrogates can underperform compared to simpler models and even linear baselines trained directly on the data.
Paper introduces DMPMs for efficient discrete data generation with sharp convergence bounds.
problem Efficient generation of discrete data with theoretical guarantees.
method Discrete Markov Probabilistic Models (DMPMs) operating in bit space with time-reversal process.
result Sharp convergence bounds established under minimal assumptions, competitive performance in discrete data generation.
It is generally difficult to make any statements about the expected prediction error in an univariate setting without further knowledge about how the data were generated. Recent work showed that knowledge about the real underlying causal structure of a data generation process has implications for various machine learni…
We study data poisoning attacks in the online setting where training items arrive sequentially, and the attacker may perturb the current item to manipulate online learning. Importantly, the attacker has no knowledge of future training items nor the data generating distribution. We formulate online data poisoning attack…
A Gaussian mixture model improves generalization for long-tailed data.
problem Optimizing generalization for rare data in long-tailed distributions.
method Suggested Gaussian mixture model and comparison of linear vs. nonlinear classifiers.
result Nonlinear classifiers outperform linear ones for long-tailed data.
TVineSynth generates synthetic data to balance privacy and utility.
problem Balancing privacy and utility in synthetic data generation.
method Uses vine copula with truncation to control privacy and utility trade-off.
result Achieves superior privacy-utility balance compared to competitors.
Bayesian methods often misinterpret data and asymptotic concepts.
problem Misunderstandings in Bayesian predictive inference.
method Discussion of two specific misunderstandings.
result Consequences of misinterpretations illustrated through examples.
A large amount of observational data has been accumulated in various fields in recent times, and there is a growing need to estimate the generating processes of these data. A linear non-Gaussian acyclic model (LiNGAM) based on the non-Gaussianity of external influences has been proposed to estimate the data-generating …
Study generates synthetic fNIRS data and applies machine learning for improved neuroimaging.
problem Scarcity of high-quality fNIRS neuroimaging datasets.
method Synthetic data generation using Monte Carlo simulations and machine learning.
result Improved accuracy and efficiency of fNIRS tomography.
Proposes DP-MERF for privacy-preserving synthetic data generation.
problem Privacy-preserving data generation for synthetic datasets.
method Differentially private mean embeddings with random features.
result Achieves better privacy-utility trade-offs than existing methods.
The paper analyzes CycleGAN's error components for unpaired data generation.
problem Analyzing approximation and estimation errors in CycleGAN for unpaired data.
method Decomposes risk into approximation and estimation errors, analyzing each separately and considering their trade-offs.
result Theoretical insights into CycleGAN's performance through error analysis.
A vine copula model is a flexible high-dimensional dependence model which uses only bivariate building blocks. However, the number of possible configurations of a vine copula grows exponentially as the number of variables increases, making model selection a major challenge in development. In this work, we formulate a v…
Paper introduces a novel method to generate diverse inputs for neural programming by example.
problem Synthesizing programs from input/output pairs using machine learning.
method Uses an SMT solver to generate diverse input-output pairs.
result Generated inputs improve model performance and generalization.
We present a general framework, the coupled compound Poisson factorization (CCPF), to capture the missing-data mechanism in extremely sparse data sets by coupling a hierarchical Poisson factorization with an arbitrary data-generating model. We derive a stochastic variational inference algorithm for the resulting model …
Estimates copula density for complex data distributions.
problem Estimating copula density from observed data.
method Neural network-based copula density neural estimation (CODINE).
result Novel approach capable of modeling complex distributions.
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.
Generative model improves tabular data density estimation.
problem Challenges in estimating tabular data distribution.
method Tensor contraction layers and transformers in VAEs.
result Embedding representations improve density estimation metrics.
This paper analyzes and improves GANs' approximation ability.
problem Theoretical and algorithmic analysis of GANs' approximation property.
method Theoretical analysis and SDG approach to enhance GANs' approximation ability.
result The generator of GANs can universally approximate the potential data distribution.
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.
Differentially private statistical inference using β-divergence.
problem Achieving differential privacy without altering data generation.
method Sampling from a generalised posterior minimizing β-divergence. result More precise inference with broader applicability.