| Malsar: Multi-task learning via structural regularization J Zhou, J Chen, J Ye Arizona State University 21, 2011 | 250 | 2011 |
| Clustered multi-task learning via alternating structure optimization J Zhou, J Chen, J Ye Advances in neural information processing systems, 702-710, 2011 | 205 | 2011 |
| Integrating low-rank and group-sparse structures for robust multi-task learning J Chen, J Zhou, J Ye Proceedings of the 17th ACM SIGKDD international conference on Knowledge …, 2011 | 185 | 2011 |
| A multi-task learning formulation for predicting disease progression J Zhou, L Yuan, J Liu, J Ye Proceedings of the 17th ACM SIGKDD international conference on Knowledge …, 2011 | 166 | 2011 |
| Modeling disease progression via fused sparse group lasso J Zhou, J Liu, VA Narayan, J Ye Proceedings of the 18th ACM SIGKDD international conference on Knowledge …, 2012 | 128 | 2012 |
| Lasso Screening Rules via Dual Polytope Projection J Wang, J Zhou, P Wonka, J Ye NIPS, 2013 | 126 | 2013 |
| Modeling disease progression via multi-task learning J Zhou, J Liu, VA Narayan, J Ye, ... NeuroImage 78, 233-248, 2013 | 118 | 2013 |
| From micro to macro: data driven phenotyping by densification of longitudinal electronic medical records J Zhou, F Wang, J Hu, J Ye Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014 | 104 | 2014 |
| Patient subtyping via time-aware LSTM networks IM Baytas, C Xiao, X Zhang, F Wang, AK Jain, J Zhou Proceedings of the 23rd ACM SIGKDD international conference on knowledge …, 2017 | 102 | 2017 |
| Analysis of sampling techniques for imbalanced data: An n= 648 ADNI study R Dubey, J Zhou, Y Wang, PM Thompson, J Ye, ... NeuroImage 87, 220-241, 2014 | 88 | 2014 |
| Who, what, when, and where: Multi-dimensional collaborative recommendations using tensor factorization on sparse user-generated data P Bhargava, T Phan, J Zhou, J Lee Proceedings of the 24th international conference on world wide web, 130-140, 2015 | 70 | 2015 |
| Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease L Zhan, J Zhou, Y Wang, Y Jin, N Jahanshad, G Prasad, TM Nir, ... Frontiers in aging neuroscience 7, 48, 2015 | 66 | 2015 |
| An rnn architecture with dynamic temporal matching for personalized predictions of parkinson's disease C Che, C Xiao, J Liang, B Jin, J Zho, F Wang Proceedings of the 2017 SIAM International Conference on Data Mining, 198-206, 2017 | 45 | 2017 |
| Learning a task-specific deep architecture for clustering Z Wang, S Chang, J Zhou, M Wang, TS Huang Proceedings of the 2016 SIAM International Conference on Data Mining, 369-377, 2016 | 45 | 2016 |
| Factorized Similarity Learning in Networks S Chang, GJ Qi, CC Aggarwal, J Zhou, M Wang, TS Huang Data Mining (ICDM), 2014 IEEE 14th International Conference on, 2014 | 43 | 2014 |
| A safe screening rule for sparse logistic regression J Wang, J Zhou, J Liu, P Wonka, J Ye Advances in neural information processing systems, 1053-1061, 2014 | 38 | 2014 |
| Efficient large-scale fleet management via multi-agent deep reinforcement learning K Lin, R Zhao, Z Xu, J Zhou Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 33 | 2018 |
| Efficient multi-task feature learning with calibration P Gong, J Zhou, W Fan, J Ye Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014 | 33 | 2014 |
| Active matrix completion S Chakraborty, J Zhou, V Balasubramanian, S Panchanathan, I Davidson, ... 2013 IEEE 13th International Conference on Data Mining, 81-90, 2013 | 31 | 2013 |
| Missing modalities imputation via cascaded residual autoencoder L Tran, X Liu, J Zhou, R Jin Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 27 | 2017 |
Jieping YeUniversity of MichiganVerified email at umich.edu
