Chengling Gao — accepted AI-conference papers

2026

HyperGOOD: Towards Out-of-Distribution Detection in Hypergraphs

Tingyi Cai, Yunliang Jiang, Ming Li, Changqin Huang, Yujie Fang, Chengling Gao +1

AAAI 2026technical

Out-of-distribution (OOD) detection plays a critical role in ensuring the robustness of machine learning models in open-world settings. While extensive efforts have been made in vision, language, and graph domains, the challenge of OOD detection in hypergraph-structured data remains unexplored. In t

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2026

Permutation Equivariant Framelet-based Hypergraph Neural Networks

Ming Li, Yi Wang, Chengling Gao, Lu Bai, Yujie Fang, Xiaosheng Zhuang +1

AAAI 2026technical

Hypergraphs provide a natural and expressive framework for modeling high-order relationships, enabling the representation of group-wise interactions beyond pairwise connections. While hypergraph neural networks (HNNs) have shown promise for learning on such structures, existing models often rely on

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