1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2025★ 1 cited
Harnessing LLMs Explanations to Boost Surrogate Models in Tabular Data Classification
Ruxue Shi, Hengrui Gu, Xu Shen +1
Large Language Models (LLMs) have shown remarkable ability in solving complex tasks, making them a promising tool for enhancing tabular learning. However, existing LLM-based method…
cs.LG2025
Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning
Ruxue Shi, Hengrui Gu, Hangting Ye +3
Few-shot tabular learning, in which machine learning models are trained with a limited amount of labeled data, provides a cost-effective approach to addressing real-world challenge…
cs.LG2025
A Comprehensive Survey of Synthetic Tabular Data Generation
Ruxue Shi, Yili Wang, Mengnan Du +3
Tabular data is one of the most prevalent and important data formats in real-world applications such as healthcare, finance, and education. However, its effective use in machine le…