| Self-Supervised Graph Transformer on Large-Scale Molecular Data Y Rong*, Y Bian*, T Xu, W Xie, Y Wei, W Huang, J Huang Advances in Neural Information Processing Systems 33, 2020 | 748 | 2020 |
| Guarantees for Greedy Maximization of Non-submodular Functions with Applications AA Bian, JM Buhmann, A Krause, S Tschiatschek ICML 2017, 2017 | 285 | 2017 |
| Guaranteed non-convex optimization: Submodular maximization over continuous domains AA Bian, B Mirzasoleiman, JM Buhmann, A Krause AISTATS 2017, 2017 | 169 | 2017 |
| Graph Information Bottleneck for Subgraph Recognition J Yu, T Xu, Y Rong, Y Bian, J Huang, R He ICLR 2021, 2020 | 163 | 2020 |
| Independent SE (3)-Equivariant Models for End-to-End Rigid Protein Docking OE Ganea, X Huang, C Bunne, Y Bian, R Barzilay, T Jaakkola, A Krause ICLR 2022 Spotlight, 2021 | 159 | 2021 |
| CoLa: Communication-Efficient Decentralized Linear Learning L He*, A Bian*, M Jaggi NeurIPS 2018, 2018 | 153* | 2018 |
| Transformer for graphs: An overview from architecture perspective E Min, R Chen, Y Bian, T Xu, K Zhao, W Huang, P Zhao, J Huang, ... arXiv preprint arXiv:2202.08455, 2022 | 141 | 2022 |
| Cross-dependent graph neural networks for molecular property prediction H Ma, Y Bian, Y Rong, W Huang, T Xu, W Xie, G Ye, J Huang Bioinformatics 38 (7), 2003-2009, 2022 | 115* | 2022 |
| Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs Y Chen, Y Zhang, Y Bian, H Yang, K Ma, B Xie, T Liu, B Han, J Cheng NeurIPS 2022 Spotlight, 2022 | 114 | 2022 |
| Continuous DR-submodular Maximization: Structure and Algorithms A Bian, K Levy, A Krause, JM Buhmann NIPS 2017, 486-496, 2017 | 107 | 2017 |
| DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-aided Drug Discovery--A Focus on Affinity Prediction Problems with Noise Annotations Y Ji, L Zhang, J Wu, B Wu, L Li, LK Huang, T Xu, Y Rong, J Ren, D Xue, ... DataPerf Workshop at ICML 2022, 2022 | 93* | 2022 |
| Divide-and-conquer: Post-user interaction network for fake news detection on social media E Min, Y Rong, Y Bian, T Xu, P Zhao, J Huang, S Ananiadou Proceedings of the ACM web conference 2022, 1148-1158, 2022 | 68 | 2022 |
| Pareto invariant risk minimization: Towards mitigating the optimization dilemma in out-of-distribution generalization Y Chen, K Zhou, Y Bian, B Xie, B Wu, Y Zhang, K Ma, H Yang, P Zhao, ... arXiv preprint arXiv:2206.07766, 2022 | 51 | 2022 |
| Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference YA Bian, JM Buhmann, A Krause ICML, 644-653, 2019 | 50* | 2019 |
| Recognizing Predictive Substructures with Subgraph Information Bottleneck J Yu, T Xu, Y Rong, Y Bian, J Huang, R He IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 | 47 | 2021 |
| Fairness-guided few-shot prompting for large language models H Ma, C Zhang, Y Bian, L Liu, Z Zhang, P Zhao, S Zhang, H Fu, Q Hu, ... Advances in Neural Information Processing Systems 36, 43136-43155, 2023 | 45 | 2023 |
| On Self-Distilling Graph Neural Network Y Chen, Y Bian, X Xiao, Y Rong, T Xu, J Huang IJCAI 2021, 2020 | 45 | 2020 |
| Not all low-pass filters are robust in graph convolutional networks H Chang, Y Rong, T Xu, Y Bian, S Zhou, X Wang, J Huang, W Zhu Advances in Neural Information Processing Systems 34, 25058-25071, 2021 | 44 | 2021 |
| 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 | 43 | 2023 |
| Beef: Bi-compatible class-incremental learning via energy-based expansion and fusion FY Wang, DW Zhou, L Liu, HJ Ye, Y Bian, DC Zhan, P Zhao The eleventh international conference on learning representations, 2022 | 42 | 2022 |
Joachim M. BuhmannETH ZurichVerified email at inf.ethz.ch
