StablePFN is proposed, a novel tabular foundation model that integrates explicit causal awareness with stable predictive modeling and significantly outperforms state-of-the-art baselines in cross-environment prediction settings, particularly in challenging high-bias scenarios.
Zheng Guan, Yikang Chen, Hao Qian et al.· Proceedings of the 32nd ACM...· 0 citations
This tutorial presents a systematic overview of this emerging paradigm of tabular foundation models, which treats tables as a common representation that can capture information from tabular data, time series, and graphs within a shared learning framework.
Peng Cui, Xing-Xuan Zhang, Han-Jia Ye et al.· Proceedings of the 32nd ACM...· 0 citations
Pre-trained tabular prediction models based on Prior-Data Fitted Networks (PFNs), such as TabPFN and LimiX, have achieved remarkable progress in supervised learning, demonstrating immense potential across real-world scenarios and diverse downstream tasks. However, a critical question remains systematically unexplored:...
Zheng Guan, Yikang Chen, Hao Qian et al.· Proceedings of the 32nd ACM...· 0 citations
GTAlign is proposed, a surprisingly simple yet effective Graph-to-Table Alignment framework for text-free Graph Foundation Model, and a community-guided continual pre-training, where pseudo-labels derived from graph community are used to construct few-shot prediction episodes.
Chunyu Hu, Tianyin Liao, Ge Lan et al.· arXiv.org· 0 citations
This tutorial presents a systematic overview of this emerging paradigm of tabular foundation models, which treats tables as a common representation that can capture information from tabular data, time series, and graphs within a shared learning framework.
Peng Cui, Xingxuan Zhang, Han-Jia Ye et al.· Proceedings of the 32nd ACM...· 0 citations
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