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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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9 results for “CTGAN”

Bayesian Generative Adversarial Networks via Gaussian Approximation for Tabular Data Synthesiscs.LG

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.

Bayesian GAN CTGAN tabular data synthesis Gaussian approximation

2026-02-25abs ↗pdf ↗

A Modified CTGAN-Plus-Features Based Method for Optimal Asset Allocationq-fin.PM

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

problem Optimizing portfolio weights in asset allocation problems.

method Combines synthetic data generation with CVaR-constraint optimization.

result Synthetic data captures key characteristics of original data and outperforms conventional strategies.

portfolio optimization asset allocation synthetic data CVaR CTGAN

2023-02-05abs ↗pdf ↗

Composite Travel Generative Adversarial Networks for Tabular and Sequential Population Synthesiscs.LG

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.

Generative Adversarial Networks Population Synthesis Transportation Modeling Agent-Based Modeling Tabular Data

2020-04-15abs ↗pdf ↗

Synthesizing Property & Casualty Ratemaking Datasets using Generative Adversarial Networksstat.ML

Generative adversarial networks create synthetic insurance datasets from confidential originals.

problem Difficulty in accessing or sharing confidential insurance datasets for research.

method Design and use of three GAN architectures tailored for multi-categorical insurance data.

result MC-WGAN-GP synthesizes the best data, CTGAN is easiest to use, and MNCDP-GAN ensures differential privacy.

Generative Adversarial Networks Insurance Ratemaking Synthetic Data Privacy Differential Privacy

2020-08-13abs ↗pdf ↗

Generative Synthesis of Insurance Datasetsstat.AP

One of the impediments in advancing actuarial research and developing open source assets for insurance analytics is the lack of realistic publicly available datasets. In this work, we develop a workflow for synthesizing insurance datasets leveraging CTGAN, a recently proposed neural network architecture for generating …

2019-12-05abs ↗pdf ↗

Trustworthy synthetic data for campaign decision support: strategy simulation fidelity and the PolicySynth frameworkcs.LG

PolicySynth improves synthetic data alignment with real data for better campaign decisions.

problem Synthetic data used in decision support systems often leads to incorrect decisions.

method PolicySynth framework that conditions synthetic data on churn scorer to align with real data decisions.

result PolicySynth achieves high strategy simulation fidelity (0.923-0.960) on churn and acquisition datasets.

synthetic data decision support strategy simulation fidelity PolicySynth churn scorer

2026-07-13abs ↗pdf ↗

Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunitiesstat.ME

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.

synthetic data causal inference average treatment effect generative models hybrid approach

2026-04-26abs ↗pdf ↗

An Augmented Surprise-guided Sequential Learning Framework for Predicting the Melt Pool Geometrycs.LG

Framework predicts melt pool geometry with AI, improving manufacturing quality.

problem Achieving consistent product quality in Metal Additive Manufacturing.

method Surprise-guided sequential learning framework integrating CTGAN for limited data.

result Enhanced predictive accuracy for melt pool dimensions.

Metal Additive Manufacturing Melt Pool Geometry Sequential Learning Conditional Tabular Generative Adversarial Network Artificial Intelligence

2024-01-10abs ↗pdf ↗

Risk In Context: Benchmarking Privacy Leakage of Foundation Models in Synthetic Tabular Data Generationcs.LG

Foundation models leak sensitive data in synthetic tabular data generation, especially LLaMA 3.3 70B.

problem Privacy leakage in synthetic tabular data generation using foundation models.

method Benchmarked three foundation models (GPT-4o-mini, LLaMA 3.3 70B, TabPFN v2) against four baselines on 35 real-world tables.

result Foundation models, especially LLaMA 3.3 70B, have the highest privacy risk in synthetic tabular data generation.

synthetic data foundation models privacy leakage membership inference downstream utility

2025-07-22abs ↗pdf ↗