| DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion Q Wu, C Yang, W Zhao, Y He, D Wipf, J Yan International Conference on Learning Representations, 2023 | 106 | 2023 |
| Simplifying and Empowering Transformers for Large-Graph Representations Q Wu, W Zhao, C Yang, H Zhang, F Nie, H Jiang, Y Bian, J Yan Advances in Neural Information Processing Systems 36, 2023 | 93 | 2023 |
| Energy-based Out-of-Distribution Detection for Graph Neural Networks Q Wu, Y Chen, C Yang, J Yan International Conference on Learning Representations, 2023 | 78 | 2023 |
| Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs C Yang, Q Wu, J Wang, J Yan International Conference on Learning Representations, 2023 | 73 | 2023 |
| Cross-Task Knowledge Distillation in Multi-Task Recommendation C Yang, J Pan, X Gao, T Jiang, D Liu, G Chen Proceedings of the AAAI Conference on Artificial Intelligence 36 (4), 4318-4326, 2022 | 45 | 2022 |
| Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach Q Wu, C Yang, J Yan Advances in Neural Information Processing Systems 34, 19435-19447, 2021 | 38 | 2021 |
| Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment C Yang, Q Wu, Q Wen, Z Zhou, L Sun, J Yan Advances in Neural Information Processing Systems 35, 22656-22670, 2022 | 28 | 2022 |
| Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks C Yang, Q Wu, J Yan Advances in Neural Information Processing Systems 35, 29761-29775, 2022 | 28 | 2022 |
| GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks W Zhao, Q Wu, C Yang, J Yan Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 18 | 2023 |
| Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach C Yang, Q Wu, J Jin, X Gao, J Pan, G Chen International Joint Conference on Artificial Intelligence, 2022 | 17 | 2022 |
| Graph Out-of-Distribution Generalization via Causal Intervention Q Wu, F Nie, C Yang, T Bao, J Yan Proceedings of the ACM on Web Conference, 850-860, 2024 | 15 | 2024 |
| Advective Diffusion Transformers for Topological Generalization in Graph Learning Q Wu, C Yang, K Zeng, F Nie, M Bronstein, J Yan arXiv preprint arXiv:2310.06417, 2024 | 6 | 2024 |
| How Graph Neural Networks Learn: Lessons from Training Dynamics C Yang, Q Wu, D Wipf, R Sun, J Yan International Conference on Machine Learning, 2024 | 5* | 2024 |
| InfoMLP: Unlocking the Potential of MLPs for Semi-Supervised Learning with Structured Data H Zhang, Q Wu, C Yang, PS Yu Proceedings of the 33rd ACM International Conference on Information and …, 2024 | | 2024 |
| Learning Divergence Fields for Shift-Robust Graph Representations Q Wu, F Nie, C Yang, J Yan International Conference on Machine Learning, 2024 | | 2024 |
Qitian WuBroad Institute of MIT and HarvardPotvrđena adresa e-pošte na broadinstitute.org