| 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 | 1197 | 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 | 381 | 2017 |
| 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 | 143 | 2020 |
| SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-task Learning J Ma, Z Zhao, J Chen, A Li, L Hong, EH Chi | 127 | 2019 |
| 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 | 111 | 2020 |
| Subgroup generalization and fairness of graph neural networks J Ma, J Deng, Q Mei Advances in Neural Information Processing Systems 34, 1048-1061, 2021 | 93 | 2021 |
| 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 | 91 | 2016 |
| 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 | 84 | 2019 |
| 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 | 58 | 2023 |
| Can llms effectively leverage graph structural information: when and why J Huang, X Zhang, Q Mei, J Ma arXiv preprint arXiv:2309.16595, 2023 | 51* | 2023 |
| 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 | 38 | 2022 |
| Soden: A scalable continuous-time survival model through ordinary differential equation networks W Tang, J Ma, Q Mei, J Zhu Journal of Machine Learning Research 23 (34), 1-29, 2022 | 38 | 2022 |
| 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 | 24 | 2020 |
| Partition-based active learning for graph neural networks J Ma, Z Ma, J Chai, Q Mei arXiv preprint arXiv:2201.09391, 2022 | 22 | 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 | 20* | 2021 |
| Graph representation learning via multi-task knowledge distillation J Ma, Q Mei arXiv preprint arXiv:1911.05700, 2019 | 19 | 2019 |
| Analyzing chain-of-thought prompting in large language models via gradient-based feature attributions S Wu, EM Shen, C Badrinath, J Ma, H Lakkaraju arXiv preprint arXiv:2307.13339, 2023 | 18 | 2023 |
| Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications Y Liu, S Gautam, J Ma, H Lakkaraju Proceedings of the 2024 Conference of the North American Chapter of the …, 2024 | 16* | 2024 |
| Fair machine unlearning: Data removal while mitigating disparities A Oesterling, J Ma, F Calmon, H Lakkaraju International Conference on Artificial Intelligence and Statistics, 3736-3744, 2024 | 14 | 2024 |
| Towards bridging the gaps between the right to explanation and the right to be forgotten S Krishna, J Ma, H Lakkaraju International Conference on Machine Learning, 17808-17826, 2023 | 14 | 2023 |
Qiaozhu MeiProfessor, University of MichiganVerified email at umich.edu
