| Modeling task relationships in multi-task learning with multi-gate mixture-of-experts J Ma, Z Zhao, X Yi, J Chen, L Hong, EH Chi Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018 | 713 | 2018 |
| Deepcas: An end-to-end predictor of information cascades C Li, J Ma, X Guo, Q Mei Proceedings of the 26th international conference on World Wide Web, 577-586, 2017 | 324 | 2017 |
| SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-task Learning J Ma, Z Zhao, J Chen, A Li, L Hong, EH Chi | 97 | 2019 |
| Towards more practical adversarial attacks on graph neural networks J Ma, S Ding, Q Mei Advances in neural information processing systems 33, 4756-4766, 2020 | 93 | 2020 |
| Joint community and structural hole spanner detection via harmonic modularity L He, CT Lu, J Ma, J Cao, L Shen, PS Yu Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge …, 2016 | 89 | 2016 |
| Off-policy learning in two-stage recommender systems J Ma, Z Zhao, X Yi, J Yang, M Chen, J Tang, L Hong, EH Chi Proceedings of The Web Conference 2020, 463-473, 2020 | 79 | 2020 |
| A flexible generative framework for graph-based semi-supervised learning J Ma, W Tang, J Zhu, Q Mei Advances in Neural Information Processing Systems 32, 2019 | 64 | 2019 |
| Subgroup generalization and fairness of graph neural networks J Ma, J Deng, Q Mei Advances in Neural Information Processing Systems 34, 1048-1061, 2021 | 55 | 2021 |
| Soden: A scalable continuous-time survival model through ordinary differential equation networks W Tang, J Ma, Q Mei, J Zhu The Journal of Machine Learning Research 23 (1), 1516-1544, 2022 | 21 | 2022 |
| Adversarial attack on graph neural networks as an influence maximization problem J Ma, J Deng, Q Mei Proceedings of the Fifteenth ACM International Conference on Web Search and …, 2022 | 18 | 2022 |
| Graph representation learning via multi-task knowledge distillation J Ma, Q Mei arXiv preprint arXiv:1911.05700, 2019 | 16 | 2019 |
| Copulagnn: Towards integrating representational and correlational roles of graphs in graph neural networks J Ma, B Chang, X Zhang, Q Mei arXiv preprint arXiv:2010.02089, 2020 | 15 | 2020 |
| Post Hoc Explanations of Language Models Can Improve Language Models S Krishna, J Ma, D Slack, A Ghandeharioun, S Singh, H Lakkaraju arXiv preprint arXiv:2305.11426, 2023 | 8 | 2023 |
| Partition-based active learning for graph neural networks J Ma, Z Ma, J Chai, Q Mei arXiv preprint arXiv:2201.09391, 2022 | 7 | 2022 |
| How Much Space Has Been Explored? Measuring the Chemical Space Covered by Databases and Machine-Generated Molecules Y Xie, Z Xu, J Ma, Q Mei arXiv preprint arXiv:2112.12542, 2021 | 7* | 2021 |
| Learning-to-rank with partitioned preference: Fast estimation for the Plackett-Luce model J Ma, X Yi, W Tang, Z Zhao, L Hong, E Chi, Q Mei International Conference on Artificial Intelligence and Statistics, 928-936, 2021 | 7 | 2021 |
| Can LLMs Effectively Leverage Graph Structural Information: When and Why J Huang, X Zhang, Q Mei, J Ma arXiv preprint arXiv:2309.16595, 2023 | 2 | 2023 |
| Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten S Krishna, J Ma, H Lakkaraju arXiv preprint arXiv:2302.04288, 2023 | 2 | 2023 |
| Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks J Ma, X Zhang, H Fan, J Huang, T Li, TW Li, Y Tu, C Zhu, Q Mei Learning on Graphs Conference, 7: 1-7: 23, 2022 | 2 | 2022 |
| ‘A new benchmark of graph learning for PM2. 5 forecasting under distribution shift Y Liu, J Ma, P Dhillon, Q Mei ACM, 6, 2021 | 2 | 2021 |
Qiaozhu MeiProfessor, University of MichiganVerified email at umich.edu
