Jiaqi Ma
Cited by
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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
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
SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-task Learning
J Ma, Z Zhao, J Chen, A Li, L Hong, EH Chi
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
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
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
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
Subgroup generalization and fairness of graph neural networks
J Ma, J Deng, Q Mei
Advances in Neural Information Processing Systems 34, 1048-1061, 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
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
Graph representation learning via multi-task knowledge distillation
J Ma, Q Mei
arXiv preprint arXiv:1911.05700, 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
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
Partition-based active learning for graph neural networks
J Ma, Z Ma, J Chai, Q Mei
arXiv preprint arXiv:2201.09391, 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
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
Can LLMs Effectively Leverage Graph Structural Information: When and Why
J Huang, X Zhang, Q Mei, J Ma
arXiv preprint arXiv:2309.16595, 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
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
‘A new benchmark of graph learning for PM2. 5 forecasting under distribution shift
Y Liu, J Ma, P Dhillon, Q Mei
ACM, 6, 2021
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