Haiyang Yu

According to our database, Haiyang Yu authored at least 18 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Tensor Decomposition Networks for Fast Machine Learning Interatomic Potential Computations.

Yuchao Lin, Cong Fu, Zachary Krueger, Haiyang Yu, Maho Nakata, Jianwen Xie, Emine Küçükbenli, Xiaofeng Qian, Shuiwang Ji

CoRR, July, 2025

Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials.

Cong Fu, Yuchao Lin, Zachary Krueger, Haiyang Yu, Maho Nakata, Jianwen Xie, Emine Küçükbenli, Xiaofeng Qian, Shuiwang Ji

CoRR, July, 2025

Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Frames.

Haiyang Yu, Yuchao Lin, Xuan Zhang, Xiaofeng Qian, Shuiwang Ji

CoRR, June, 2025

NeurIPS 2024 ML4CFD Competition: Results and Retrospective Analysis.

Mouadh Yagoubi, David Danan, Milad Leyli-Abadi, Jocelyn Ahmed Mazari, Jean-Patrick Brunet, Abbas Kabalan, Fabien Casenave, Yuxin Ma, Giovanni Catalani, Jean Fesquet, Jacob Helwig, Xuan Zhang, Haiyang Yu, Xavier Bertrand, Frederic Tost, Michael Bauerheim, Joseph Morlier, Shuiwang Ji

CoRR, June, 2025

A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modeling.

Jacob Helwig, Sai Sreeharsha Adavi, Xuan Zhang, Yuchao Lin, Felix S. Chim, Luke Takeshi Vizzini, Haiyang Yu, Muhammad Hasnain, Saykat Kumar Biswas, John J. Holloway, Narendra Singh, N. K. Anand, Swagnik Guhathakurta, Shuiwang Ji

CoRR, June, 2025

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems.

Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Alex Strasser, Haiyang Yu, Yuqing Xie, Xiang Fu, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik J. Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji

Found. Trends Mach. Learn., 2025

Learning to Discover Regulatory Elements for Gene Expression Prediction.

Xingyu Su, Haiyang Yu, Degui Zhi, Shuiwang Ji

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithm.

Meng Liu, Haiyang Yu, Shuiwang Ji

Trans. Mach. Learn. Res., 2024

Equivariant Graph Network Approximations of High-Degree Polynomials for Force Field Prediction.

Zhao Xu, Haiyang Yu, Montgomery Bohde, Shuiwang Ji

Trans. Mach. Learn. Res., 2024

A Geometry-Aware Message Passing Neural Network for Modeling Aerodynamics over Airfoils.

Jacob Helwig, Xuan Zhang, Haiyang Yu, Shuiwang Ji

CoRR, 2024

2023

Explainability in Graph Neural Networks: A Taxonomic Survey.

Hao Yuan, Haiyang Yu, Shurui Gui, Shuiwang Ji

IEEE Trans. Pattern Anal. Mach. Intell., May, 2023

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules.

Haiyang Yu, Meng Liu, Youzhi Luo, Alex Strasser, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian.

Haiyang Yu, Zhao Xu, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji

Proceedings of the International Conference on Machine Learning, 2023

2022

Your Neighbors Are Communicating: Towards Powerful and Scalable Graph Neural Networks.

Meng Liu, Haiyang Yu, Shuiwang Ji

CoRR, 2022

Frontiers of Graph Neural Networks with DIG.

Shuiwang Ji, Meng Liu, Yi Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Zhao Xu, Haiyang Yu

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

GraphFM: Improving Large-Scale GNN Training via Feature Momentum.

Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, Shuiwang Ji

Proceedings of the International Conference on Machine Learning, 2022

2021

DIG: A Turnkey Library for Diving into Graph Deep Learning Research.

Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Haiyang Yu, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora Oztekin, Xuan Zhang, Shuiwang Ji

J. Mach. Learn. Res., 2021

On Explainability of Graph Neural Networks via Subgraph Explorations.

Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, Shuiwang Ji

Proceedings of the 38th International Conference on Machine Learning, 2021