arXiv research

AI techniques explain synthetic tabular data weaknesses.

Proposes DP-MERF for privacy-preserving synthetic data generation.

XAI-Bench releases synthetic datasets for evaluating feature attribution methods.

Improved preterm prediction using synthetic EHG signals.

SynthETIC simulates insurance claims with customizable features.

SHAP Distance assesses semantic fidelity of synthetic tabular data.

Proposes a method for explaining tabular data using copulas.

Paper proposes a method to generate synthetic anomalies for robust anomaly detection.

Synthetic Ricci flows defined for metric measure spaces.

We explore several oversampling techniques for an imbalanced multi-label classification problem, a setting often encountered when developing models for Computer-Aided Diagnosis (CADx) systems. While most CADx systems aim to optimize classifiers for overall accuracy without considering the relative distribution of each …

Synthetic telematics dataset created from insurance claims data.

AutoDiff combines auto-encoder and diffusion model for realistic tabular data synthesis.

New method generates geolocated synthetic populations from real data.

An important problem in machine learning and statistics is to identify features that causally affect the outcome. This is often impossible to do from purely observational data, and a natural relaxation is to identify features that are correlated with the outcome even conditioned on all other observed features. For exam…

Study improves feature acquisition for static settings in AFAPE.

FlowSelect uses normalizing flows to control FDR in feature selection.

CardiCat generates synthetic data for high-cardinality tabular datasets.

This paper shows feature importance remains valid even in low-performing models.

Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, structured data handled by deep learning models. In this paper, we propose D…

Enhanced Extended Isolation Forest (EIF+) improves anomaly detection and provides interpretable explanations.

A novel model-selection method for dynamic networks using synthetic data.

Modified CTGAN-Plus-Features method optimizes asset allocation with CVaR constraint.

EmDT generates synthetic fraud data to improve detection accuracy.

Deep learning with Convolutional Neural Networks has shown great promise in various areas of image-based classification and enhancement but is often unsuitable for predictive modeling involving non-image based features or features without spatial correlations. We present a novel approach for representation of high dime…

We propose a single neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining features in "one shot". The features may be both real-valued and categorical. Training of the model is performed by stochastic variational Bay…

We prove that, under low noise assumptions, the support vector machine with $N\ll m$ random features (RFSVM) can achieve the learning rate faster than $O(1/\sqrt{m})$ on a training set with $m$ samples when an optimized feature map is used. Our work extends the previous fast rate analysis of random features method from…

We introduce a method called multi-scale local shape analysis, or MLSA, for extracting features that describe the local structure of points within a dataset. The method uses both geometric and topological features at multiple levels of granularity to capture diverse types of local information for subsequent machine lea…

Emotion recognition is a classic field of research with a typical setup extracting features and feeding them through a classifier for prediction. On the other hand, generative models jointly capture the distributional relationship between emotions and the feature profiles. Relatively recently, Generative Adversarial Ne…

The Generative Adversarial Network (GAN) has achieved great success in generating realistic (real-valued) synthetic data. However, convergence issues and difficulties dealing with discrete data hinder the applicability of GAN to text. We propose a framework for generating realistic text via adversarial training. We emp…

Proposes $φ$-table for statistical SHAP explanations in regression models.

Contrastive ICA identifies features in experimental groups relative to controls.

We propose a principled method for kernel learning, which relies on a Fourier-analytic characterization of translation-invariant or rotation-invariant kernels. Our method produces a sequence of feature maps, iteratively refining the SVM margin. We provide rigorous guarantees for optimality and generalization, interpret…

CTSyn generates high-quality synthetic tabular data.

Paper establishes utility theory for synthetic data generation.

A new method combines generative and feature-based approaches for unsupervised anomaly detection.

Solves multi-class imbalanced data problem with geometry-based sampling and synthetic data.

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…

Markov boundary improves tabular prediction but not as expected.

Synthetic learning improves neonatal brain MRI segmentation robustness.

We propose Power Slow Feature Analysis, a gradient-based method to extract temporally slow features from a high-dimensional input stream that varies on a faster time-scale, as a variant of Slow Feature Analysis (SFA) that allows end-to-end training of arbitrary differentiable architectures and thereby significantly ext…

Machine learning models perform better with location coordinates alone, not Moran Eigenvectors.

We propose a learning-based filter that allows us to directly modify a synthetic speech waveform into a natural speech waveform. Speech-processing systems using a vocoder framework such as statistical parametric speech synthesis and voice conversion are convenient especially for a limited number of data because it is p…

Consider a data set collected by (individuals-features) pairs in different times. It can be represented as a tensor of three dimensions (Individuals, features and times). The tensor biclustering problem computes a subset of individuals and a subset of features whose signal trajectories over time lie in a low-dimensiona…

Enhanced framework selects features for unbiased causal inference.

Magnitude-based features capture interactions between different entities in multispecies spatial data.

BSTabDiff: Block-Subunit Diffusion Priors for HDLSS Tabular Data Generation

We propose a method for feature selection that employs kernel-based measures of independence to find a subset of covariates that is maximally predictive of the response. Building on past work in kernel dimension reduction, we show how to perform feature selection via a constrained optimization problem involving the tra…

CorGAN generates synthetic healthcare records while preserving privacy.