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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.LG2025
CTSyn: A Foundation Model for Cross Tabular Data Generation
Xiaofeng Lin, Chenheng Xu, Matthew Yang +1
Generative Foundation Models (GFMs) have achieved remarkable success in producing high-quality synthetic data for images and text. However, their application to tabular data presen…
cs.LG2024
Theoretical Understanding of In-Context Learning in Shallow Transformers with Unstructured Data
Yue Xing, Xiaofeng Lin, Chenheng Xu +3
Large language models (LLMs) are powerful models that can learn concepts at the inference stage via in-context learning (ICL). While theoretical studies, e.g., \cite{zhang2023train…