AutoDiff combines auto-encoder and diffusion model for realistic tabular data synthesis.
problem Generating realistic synthetic tabular data with heterogeneous features.
method Employing auto-encoder architecture to handle tabular data's complexity.
result Synthetic tables from AutoDiff show good statistical fidelity and perform well in machine learning tasks.
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.
Orion-Bix combines biaxial attention and meta-learning for tabular few-shot learning.
problem Scaling and generalizing tabular models with mixed numeric and categorical fields, weak feature structure, and limited labeled data.
method Orion-Bix uses biaxial attention and meta-learned in-context reasoning to efficiently capture local and global dependencies.
result Orion-Bix outperforms gradient-boosting baselines and state-of-the-art tabular models on public benchmarks.
UnmaskingTrees improves tabular data imputation and generation using gradient-boosted decision trees.
problem Traditional methods outperform advanced deep learning techniques on tabular data imputation benchmarks.
method UnmaskingTrees employs gradient-boosted decision trees to incrementally unmask features for imputation and generation.
result UnmaskingTrees outperforms state-of-the-art methods on tabular imputation and generation benchmarks.
CTSyn generates high-quality synthetic tabular data.
problem Challenges in generating high-quality synthetic tabular data.
method Diffusion-based generative foundation model with autoencoder and conditional latent diffusion.
result CTSyn outperforms existing table synthesizers on standard benchmarks.
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.
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.
PyTorch Frame simplifies multi-modal tabular learning with modular data and model handling.
problem Handling complex multi-modal tabular data in deep learning.
method A PyTorch-based framework that provides a data structure, model abstraction, and integration with external models.
result Demonstrated the effectiveness of PyTorch Frame in implementing and applying diverse tabular models to complex multi-modal tabular data.
Paper tackles test-time adaptation for tabular data.
problem Performance degradation due to distribution shifts in testing.
method Proposes FTAT for robustly adapting tabular models during testing.
result FTAT outperforms state-of-the-art methods on benchmark datasets.
GAMformer bridges tabular models and interpretability, offering a single-pass approach.
problem Lack of interpretability in tabular foundation models like TabPFN.
method In-context learning for GAM shape functions, training on synthetic data.
result GAMformer performs comparably to other leading GAMs across various classification benchmarks.
GEM-T generates synthetic tabular data by fitting moments, outperforming neural networks.
problem Generating synthetic tabular data from limited or sensitive real-world data.
method Generative Entropy Maximization (MaxEnt) for tables, capturing nth-order interactions.
result GEM-T matches or exceeds deep neural network approaches in 23 out of 34 datasets.
AugMask trains diffusion models on incomplete tabular data by augmenting missing values and applying denoising supervision.
problem Training diffusion models on incomplete tabular data with missing values.
method AugMask uses stochastic augmentation and denoising supervision to adapt diffusion models to incomplete data.
result AugMask enables diffusion-based tabular generators to outperform specialized missing-aware baselines across various datasets and missingness regimes.
BiSHop tackles tabular data challenges with sparse Hopfield layers.
problem Non-rotationally invariant data structure and feature sparsity in tabular data.
method Sequential column-wise and row-wise processing through interconnected directional learning modules with generalized sparse modern Hopfield layers.
result BiSHop surpasses current SOTA methods with significantly less hyperparameter tuning.
Proposes CDTD, a diffusion model for mixed-type tabular data.
problem Adapting diffusion models to mixed-type tabular data.
method Score matching and score interpolation for continuous features, adaptive noise schedules for categorical features.
result Consistently outperforms state-of-the-art models in mixed-type tabular data.
This paper introduces DPI, a new metric to assess data-copying risk in tabular data.
problem Measuring privacy risk of data-copying in tabular generative models.
method Proposes Data Plagiarism Index (DPI) for evaluating data-copying risk.
result DPI identifies data-copying threats to tabular data models, highlighting privacy and fairness issues.
A new method generates mixed-type features in tabular data with improved realism and accuracy.
problem Generating mixed-type features combining discrete and continuous data is challenging.
method A cascaded approach: first generates low-resolution categorical and coarse numerical features, then uses these in a high-resolution flow matching model.
result The model significantly improves detection scores, generating more realistic samples and capturing distributional details.
AgEBO-Tabular combines NAS and hyperparameter tuning for fast, high-performing tabular models.
problem Developing high-performing predictive models for large tabular data sets is challenging.
method Combines aging evolution NAS and asynchronous Bayesian optimization for hyperparameter tuning in data-parallel training.
result Automatically discovered neural network models outperform state-of-the-art AutoML ensembles in inference speed by two orders of magnitude.
FinDiff generates synthetic financial data for regulatory tasks.
problem Sharing microdata for research due to privacy regulations.
method Diffusion model using embedding encodings for mixed modality financial data.
result FinDiff excels in generating high-fidelity, privacy-preserving synthetic financial data.
A framework evaluates synthetic tabular data quality objectively.
problem Lack of an objective interpretation of tabular data metrics.
method Proposes a single mathematical objective for synthetic tabular data distribution, structurally decomposes it, and unifies existing metrics.
result Synthesizers that represent tabular structure outperform other methods, especially on smaller datasets.
CardiCat generates synthetic data for high-cardinality tabular datasets.
problem Learning complexities of high-cardinality categorical features in tabular data.
method Substitutes one-hot encoding with regularized dual encoder-decoder embedding layers.
result Generates high-quality synthetic data with a smaller parameter space.
ProtoNAM models tabular data with neural networks, making predictions transparent.
problem Tabular data analysis using neural networks lacks transparency and accuracy compared to tree-based methods.
method ProtoNAM introduces prototypes into neural networks to model tabular data while maintaining explainability.
result ProtoNAM outperforms existing NN-based GAMs and provides insights into learned feature patterns.
Unified diffusion model tackles missing data in tabular datasets.
problem Training diffusion models on tabular data with missing values.
method Proposes a masked regression loss for Denoising Score Matching to learn from incomplete data.
result Demonstrates superior performance on tabular datasets with missing values compared to state-of-the-art methods.
Security of machine learning models is a concern as they may face adversarial attacks for unwarranted advantageous decisions. While research on the topic has mainly been focusing on the image domain, numerous industrial applications, in particular in finance, rely on standard tabular data. In this paper, we discuss the…
Generative adversarial networks (GANs) implicitly learn the probability distribution of a dataset and can draw samples from the distribution. This paper presents, Tabular GAN (TGAN), a generative adversarial network which can generate tabular data like medical or educational records. Using the power of deep neural netw…
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.
CTGAN synthesizes population data for travel behavior simulation.
problem Synthesizing population data for agent-based transportation modeling.
method Composite Travel Generative Adversarial Network (CTGAN).
result Consistent and accurate generation of synthetic populations with tabular and sequential mobility data.
Deep tabular models outperform GBDT in medical diagnosis tasks.
problem Transfer learning for tabular data in medical diagnosis.
method Proposes a pseudo-feature method for transfer learning between different feature sets.
result Tabular neural networks outperform GBDT in medical diagnosis tasks.
DP-FedTabDiff generates private synthetic tabular data using diffusion models and differential privacy.
problem Privacy-preserving synthetic data generation for tabular data in regulated domains.
method Combines Differential Privacy, Federated Learning, and Denoising Diffusion Probabilistic Models.
result Achieves significant privacy improvements without compromising data quality.
Generative Adversarial Network model for class-imbalanced tabular data.
problem Class imbalance in binary classification problems.
method Generative Adversarial Network (GAN) with synthetic minority class samples.
result Improves average precision compared to re-weighting and oversampling techniques.
TabDPT combines ICL and self-supervised learning to train tabular models on real data.
problem Slow generalization of tabular foundation models to unseen datasets.
method In-Context Learning (ICL) combined with self-supervised learning, using real data for pre-training.
result TabDPT achieves strong performance on regression and classification benchmarks.
New deep learning model interprets tabular data with variable selection and explainability.
problem Deep learning models lack interpretability and variable selection.
method Proposes a new network architecture that combines deep learning with generalized linear models.
result The model provides superior predictive power and interpretable results.
GACTGAN synthesizes tabular data better with less computational overhead.
problem Synthesizing mixed tabular data while balancing risk and utility.
method Integrates Bayesian posterior approximation with Stochastic Weight Averaging-Gaussian (SWAG) in CTGAN.
result GACTGAN produces better synthetic data with reduced privacy risk.
TANGOS improves neural network performance on tabular data by encouraging neuron specialization.
problem Improving neural network performance on tabular data.
method Gradient orthogonality and specialization of latent units.
result TANGOS leads to improved out-of-sample generalization performance.
SurvFM-RMST converts survival outcomes into pseudo-observation targets for tabular models.
problem Right-censored follow-up prevents direct use of survival labels in tabular patient data.
method SurvFM-RMST framework that converts survival outcomes into jackknife pseudo-observation targets for restricted mean survival time.
result SurvFM-RMST accurately recovered restricted event-free time in simulations and outperformed naive targets in static datasets.
JoLT uses LLMs to make probabilistic predictions on tabular data.
problem Making probabilistic predictions on tabular data efficiently and without preprocessing.
method JoLT leverages LLMs' in-context learning to define joint distributions over tabular data.
result JoLT outperforms other methods on tabular classification and regression tasks.
ReTabSyn synthesizes realistic tabular data efficiently by focusing on conditional distribution.
problem Synthesizing realistic tabular data in low-data, imbalanced settings.
method ReTabSyn uses reinforcement learning to prioritize feature correlation preservation during training.
result ReTabSyn consistently outperforms state-of-the-art baselines across various benchmarks.
Tabular foundation models outperform other methods in conditional density estimation across various datasets.
problem Estimating the full conditional distribution of a response given tabular covariates, especially in settings with heteroscedasticity, multimodality, or asymmetric uncertainty.
method Benchmarked three tabular foundation model variants (TabPFN and TabICL) against six CDE baselines on 39 real-world datasets.
result Tabular foundation models achieve the best CDE loss, log-likelihood, and CRPS across all sample sizes, outperforming other methods.
LLM embeddings improve adaptation to tabular Y∣X-shifts with few labeled examples.
problem Improving robustness to Y∣X-shifts in tabular data. method Serializing tabular data to LLM embeddings and fine-tuning for adaptation.
result LLM embeddings can be adapted to target domains with minimal labeled data.
Synth-MIA assesses privacy leakage in synthetic tabular data models.
problem Challenges in evaluating privacy leakage in synthetic tabular data.
method Unified threat framework deploying multiple attacks.
result Higher synthetic data quality correlates with greater privacy leakage.
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.
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.
Synthetic tabular data synthesis models balance utility and risk.
problem Generating synthetic tabular data for regulated domains.
method Latent flow models with various learning targets, paths, and sampling methods.
result Velocity and posterior matching objectives yield higher utility, while score and noise matching achieve lower risk.
DNF-Net tackles tabular data challenges with neural architecture.
problem Handling tabular data efficiently using neural networks.
method DNF-Net uses a neural architecture with inductive bias corresponding to logical Boolean formulas in disjunctive normal form over affine soft-threshold decision terms.
result DNF-Net significantly outperforms fully connected networks on tabular data.
AdapTable adapts tabular models to shifts without source data, improving HELOC performance.
problem Distribution shifts in tabular data threaten model performance.
method Shift-aware uncertainty calibrator and label distribution handler.
result Up to 16% improvement on HELOC dataset.
Fed-TDA augments federated tabular data to improve performance and privacy.
problem Non-IID data challenges in federated learning.
method Synthesizes tabular data using simple statistics for augmentation.
result Fed-TDA improves test performance and communication efficiency.
Tab-TRM uses recursive model for insurance pricing on tabular data.
problem Insurance pricing on tabular data.
method Adapts recursive latent reasoning to insurance modeling using a compact, parameter-efficient network.
result Improves insurance pricing accuracy using iterative refinement of latent tokens.
GOTabPFN improves tabular model performance with compact tokenization for HDLSS data.
problem Making tabular models effective for high-dimensional, low-sample size data without retraining.
method Introducing Graph-guided Ordering with Local Refinement (GO-LR) and Neuro-Inspired Subunit Compression (NSC) to create compact meta-features.
result GOTabPFN improves stability and accuracy in tabular benchmarks with compact tokenization.
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.