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cs.LG2026
SYNTHONY: A Stress-Aware, Intent-Conditioned Agent for Deep Tabular Generative Models Selection
Hochan Son, Xiaofeng Lin, Jason Ni +1
Deep generative models for tabular data (GANs, diffusion models, and LLM-based generators) exhibit highly non-uniform behavior across datasets; the best-performing synthesizer fami…
cs.LG2026
Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting
Joshua Ward, Chi-Hua Wang, Guang Cheng
Synthetic tabular data has gained attention for enabling privacy-preserving data sharing. While substantial progress has been made in single-table synthetic generation where data a…
cs.LG2025
When Tables Leak: Attacking String Memorization in LLM-Based Tabular Data Generation
Joshua Ward, Bochao Gu, Chi-Hua Wang +1
Large Language Models (LLMs) have recently demonstrated remarkable performance in generating high-quality tabular synthetic data. In practice, two primary approaches have emerged f…