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 (ICLR), 2023 | 43 | 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 (ICLR), 2023 | 40 | 2023 |
Energy-based Out-of-Distribution Detection for Graph Neural Networks Q Wu, Y Chen, C Yang, J Yan International Conference on Learning Representations (ICLR), 2023 | 40 | 2023 |
Cross-Task Knowledge Distillation in Multi-Task Recommendation C Yang, J Pan, X Gao, T Jiang, D Liu, G Chen AAAI Conference on Artificial Intelligence (AAAI), 2022 | 37 | 2022 |
Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach Q Wu, C Yang, J Yan Advances in Neural Information Processing Systems (NeurIPS), 2021 | 25 | 2021 |
Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks C Yang, Q Wu, J Yan Advances in Neural Information Processing Systems (NeurIPS), 2022 | 18 | 2022 |
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 (NeurIPS), 2022 | 15 | 2022 |
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 (IJCAI), 2022 | 15 | 2022 |
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 (NeurIPS), 2023 | 12 | 2023 |
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 | 3 | 2024 |
Graph Out-of-Distribution Generalization via Causal Intervention Q Wu, F Nie, C Yang, T Bao, J Yan The Web Conference (WWW), 2024 | | 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 (ICML), 2024 | | 2024 |