| 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 | 407 | 2020 |
| Guarantees for Greedy Maximization of Non-submodular Functions with Applications AA Bian, JM Buhmann, A Krause, S Tschiatschek ICML 2017, 2017 | 248 | 2017 |
| Guaranteed non-convex optimization: Submodular maximization over continuous domains AA Bian, B Mirzasoleiman, JM Buhmann, A Krause AISTATS 2017, 2017 | 149 | 2017 |
| CoLa: Communication-Efficient Decentralized Linear Learning L He*, A Bian*, M Jaggi NeurIPS 2018, 2018 | 126* | 2018 |
| 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 | 86 | 2021 |
| Continuous DR-submodular Maximization: Structure and Algorithms A Bian, K Levy, A Krause, JM Buhmann NIPS 2017, 486-496, 2017 | 81 | 2017 |
| Graph Information Bottleneck for Subgraph Recognition J Yu, T Xu, Y Rong, Y Bian, J Huang, R He ICLR 2021, 2020 | 80 | 2020 |
| 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 | 68* | 2022 |
| 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 | 41 | 2022 |
| Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference YA Bian, JM Buhmann, A Krause ICML, 644-653, 2019 | 37* | 2019 |
| A Distributed Second-Order Algorithm You Can Trust C Dünner, A Lucchi, M Gargiani, A Bian, T Hofmann, M Jaggi ICML 2018, 2018 | 34 | 2018 |
| 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 | 31* | 2022 |
| On Self-Distilling Graph Neural Network Y Chen, Y Bian, X Xiao, Y Rong, T Xu, J Huang IJCAI 2021, 2020 | 30 | 2020 |
| 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 | 27 | 2022 |
| 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 | 26 | 2021 |
| 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 | 22 | 2021 |
| Invariance Principle Meets Out-of-Distribution Generalization on Graphs Y Chen, Y Zhang, Y Bian, H Yang, K Ma, B Xie, T Liu, B Han, J Cheng ICML Workshop on Spurious Correlations, Invariance and Stability, 2022 | 20 | 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 | 19 | 2022 |
| Continuous submodular function maximization Y Bian, JM Buhmann, A Krause arXiv preprint arXiv:2006.13474, 2020 | 17 | 2020 |
| Fine-tuning graph neural networks via graph topology induced optimal transport J Zhang, X Xiao, LK Huang, Y Rong, Y Bian IJCAI 2022, 2022 | 16 | 2022 |
Junzhou Huang, FAIMBEJenkins Garrett Professor, Computer Science and Engineering, the University of Texas at ArlingtonVerified email at uta.edu
