| A model-based approach to attributed graph clustering Z Xu, Y Ke, Y Wang, H Cheng, J Cheng Proceedings of the 2012 ACM SIGMOD international conference on management of …, 2012 | 419 | 2012 |
| GBAGC: A general Bayesian framework for attributed graph clustering Z Xu, Y Ke, Y Wang, H Cheng, J Cheng ACM Transactions on Knowledge Discovery from Data (TKDD) 9 (1), 1-43, 2014 | 85 | 2014 |
| Agile and accurate CTR prediction model training for massive-scale online advertising systems Z Xu, D Li, W Zhao, X Shen, T Huang, X Li, P Li Proceedings of the 2021 international conference on management of data, 2404 …, 2021 | 43 | 2021 |
| Efficient attribute-constrained co-located community search J Luo, X Cao, X Xie, Q Qu, Z Xu, CS Jensen 2020 IEEE 36th International Conference on Data Engineering (ICDE), 1201-1212, 2020 | 23 | 2020 |
| Effective and efficient spectral clustering on text and link data Z Xu, Y Ke Proceedings of the 25th ACM International on Conference on Information and …, 2016 | 23 | 2016 |
| Towards better generalization of adaptive gradient methods Y Zhou, B Karimi, J Yu, Z Xu, P Li Advances in Neural Information Processing Systems 33, 810-821, 2020 | 21 | 2020 |
| Convergence analysis of gradient descent for eigenvector computation Z Xu, X Cao, X Gao International Joint Conferences on Artificial Intelligence, 2018 | 16 | 2018 |
| Towards practical alternating least-squares for CCA Z Xu, P Li Advances in Neural Information Processing Systems 32, 2019 | 15 | 2019 |
| Group representation theory for knowledge graph embedding C Cai, Y Cai, M Sun, Z Xu arXiv preprint arXiv:1909.05100, 2019 | 13 | 2019 |
| On truly block eigensolvers via riemannian optimization Z Xu, X Gao International Conference on Artificial Intelligence and Statistics, 168-177, 2018 | 11 | 2018 |
| Gradient descent meets shift-and-invert preconditioning for eigenvector computation Z Xu Advances in Neural Information Processing Systems 31, 2018 | 10 | 2018 |
| Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering Z Xu, J Cheng, X Xiao, R Fujimaki, Y Muraoka Knowledge and Information Systems 53, 239-268, 2017 | 10 | 2017 |
| Unsupervised video domain adaptation: A disentanglement perspective P Wei, L Kong, X Qu, X Yin, Z Xu, J Jiang, Z Ma arXiv preprint arXiv:2208.07365, 2022 | 9 | 2022 |
| A practical Riemannian algorithm for computing dominant generalized Eigenspace Z Xu, P Li Conference on Uncertainty in Artificial Intelligence, 819-828, 2020 | 9 | 2020 |
| Stochastic variance reduced Riemannian eigensolver Z Xu, Y Ke arXiv preprint arXiv:1605.08233, 2016 | 6 | 2016 |
| A Comprehensively Tight Analysis of Gradient Descent for PCA Z Xu, P Li Advances in Neural Information Processing Systems 34, 21935-21946, 2021 | 5 | 2021 |
| Faster noisy power method Z Xu, P Li International Conference on Algorithmic Learning Theory, 1138-1164, 2022 | 4 | 2022 |
| On the riemannian search for eigenvector computation Z Xu, P Li Journal of Machine Learning Research 22 (249), 1-46, 2021 | 4 | 2021 |
| Matrix eigen-decomposition via doubly stochastic riemannian optimization Z Xu, P Zhao, J Cao, X Li International conference on machine learning, 1660-1669, 2016 | 4 | 2016 |
| Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels Jian Chen, Ruiyi Zhang, Tong Yu, Rohan Sharma, Zhiqiang Xu, Tong Sun ... Thirty-seventh Conference on Neural Information Processing Systems, 2023, 2023 | 3* | 2023 |
