Ruiting Zhao — accepted AI-conference papers

2026

HyperAim: Hypergraph Contrastive Learning with Adaptive Multi-frequency Filters

Ming Li, Ruiting Zhao, Zihao Yan, Lu Bai, Lixin Cui, Feilong Cao

AAAI 2026technical

Unsupervised hypergraph representation learning has recently gained traction for its ability to model complex high-order interactions without requiring labeled data. However, existing contrastive learning methods typically overlook the frequency diversity inherent in hypergraph signals. To address t

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2026

Towards Hierarchy–Uniformity Equilibrium: Recovering Semantic Depth in Hypergraph Contrastive Learning

Ruiting Zhao, Ming Li, Lixin Cui, Lu Bai, Feilong Cao, Ke Lv +1

ICML 2026oral

Hypergraph contrastive learning is an effective paradigm for representation learning on higher-order relational data, yet existing methods largely ignore that hyperedges link nodes with multi-level semantics. Standard contrastive objectives emphasize instance discrimination via hyperspherical unifor…

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