Chenxiao Yang

发表

DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion

Junchi Yan, David Wipf, Qitian Wu, 2023, ICLR.

How Graph Neural Networks Learn: Lessons from Training Dynamics in Function Space

David Wipf, Qitian Wu, Chenxiao Yang, 2023, ArXiv.

GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks

Junchi Yan, Wentao Zhao, Qitian Wu, 2023, KDD.

Energy-based Out-of-Distribution Detection for Graph Neural Networks

Junchi Yan, Qitian Wu, Chenxiao Yang, 2023, ICLR.

Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks

Junchi Yan, Qitian Wu, Chenxiao Yang, 2022, NeurIPS.

Graph Out-of-Distribution Generalization via Causal Intervention

Qitian Wu, Fan Nie, Junchi Yan, 2024, WWW.

Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment

Junchi Yan, Qitian Wu, Qingsong Wen, 2022, NeurIPS.

Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach

Junchi Yan, Qitian Wu, Chenxiao Yang, 2021, NeurIPS.

Cross-Task Knowledge Distillation in Multi-Task Recommendation

Guihai Chen, Xiaofeng Gao, Tingyu Jiang, 2022, AAAI.

Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach

Guihai Chen, Xiaofeng Gao, Qitian Wu, 2022, IJCAI.

Advective Diffusion Transformers for Topological Generalization in Graph Learning

Qitian Wu, Chenxiao Yang, Kaipeng Zeng, 2023, ArXiv.

Simplifying and Empowering Transformers for Large-Graph Representations

Junchi Yan, Yatao Bian, Qitian Wu, 2023, NeurIPS.

Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs

Junchi Yan, Qitian Wu, Jiahua Wang, 2022, ArXiv.