| Combining link and content for community detection: a discriminative approach T Yang, R Jin, Y Chi, S Zhu Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009 | 454 | 2009 |
| Hetero-convlstm: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data Z Yuan, X Zhou, T Yang Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018 | 448 | 2018 |
| Nyström method vs random fourier features: A theoretical and empirical comparison T Yang, YF Li, M Mahdavi, R Jin, ZH Zhou Advances in neural information processing systems 25, 2012 | 438 | 2012 |
| Detecting communities and their evolutions in dynamic social networks—a Bayesian approach T Yang, Y Chi, S Zhu, Y Gong, R Jin Machine learning 82, 157-189, 2011 | 415 | 2011 |
| Weakly-convex–concave min–max optimization: provable algorithms and applications in machine learning H Rafique, M Liu, Q Lin, T Yang Optimization Methods and Software 37 (3), 1087-1121, 2022 | 300* | 2022 |
| Trading regret for efficiency: online convex optimization with long term constraints M Mahdavi, R Jin, T Yang The Journal of Machine Learning Research 13 (1), 2503-2528, 2012 | 288 | 2012 |
| Online AUC maximization P Zhao, SCH Hoi, R Jin, T Yang International Machine Learning Society, 2011 | 278 | 2011 |
| Online optimization with gradual variations CK Chiang, T Yang, CJ Lee, M Mahdavi, CJ Lu, R Jin, S Zhu Conference on Learning Theory, 6.1-6.20, 2012 | 263 | 2012 |
| Learning attributes equals multi-source domain generalization C Gan, T Yang, B Gong Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 250 | 2016 |
| Unified convergence analysis of stochastic momentum methods for convex and non-convex optimization T Yang, Q Lin, Z Li arXiv preprint arXiv:1604.03257, 2016 | 240* | 2016 |
| Hyper-class augmented and regularized deep learning for fine-grained image classification S Xie, T Yang, X Wang, Y Lin Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 236 | 2015 |
| A machine learning approach for air quality prediction: Model regularization and optimization D Zhu, C Cai, T Yang, X Zhou Big data and cognitive computing 2 (1), 5, 2018 | 200 | 2018 |
| Trading computation for communication: Distributed stochastic dual coordinate ascent T Yang Advances in neural information processing systems 26, 2013 | 183 | 2013 |
| Large-scale robust deep auc maximization: A new surrogate loss and empirical studies on medical image classification Z Yuan, Y Yan, M Sonka, T Yang Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 155 | 2021 |
| Tracking slowly moving clairvoyant: Optimal dynamic regret of online learning with true and noisy gradient T Yang, L Zhang, R Jin, J Yi International Conference on Machine Learning, 449-457, 2016 | 149 | 2016 |
| Online multiple kernel classification SCH Hoi, R Jin, P Zhao, T Yang Machine learning 90, 289-316, 2013 | 146 | 2013 |
| First-order stochastic algorithms for escaping from saddle points in almost linear time Y Xu, R Jin, T Yang Advances in neural information processing systems 31, 2018 | 144 | 2018 |
| Improved dynamic regret for non-degenerate functions L Zhang, T Yang, J Yi, R Jin, ZH Zhou Advances in Neural Information Processing Systems 30, 2017 | 135 | 2017 |
| Distributed stochastic variance reduced gradient methods by sampling extra data with replacement JD Lee, Q Lin, T Ma, T Yang Journal of Machine Learning Research 18 (122), 1-43, 2017 | 131* | 2017 |
| Dynamic regret of strongly adaptive methods L Zhang, T Yang, ZH Zhou International conference on machine learning, 5882-5891, 2018 | 119 | 2018 |
Rong JinAlibaba GroupVerified email at cse.msu.edu
