| MILES: Multiple-instance learning via embedded instance selection Y Chen, J Bi, JZ Wang IEEE transactions on pattern analysis and machine intelligence 28 (12), 1931 …, 2006 | 975 | 2006 |
| End-to-end structure-aware convolutional networks for knowledge base completion C Shang, Y Tang, J Huang, J Bi, X He, B Zhou Proceedings of the AAAI conference on artificial intelligence 33 (01), 3060-3067, 2019 | 701 | 2019 |
| Dimensionality reduction via sparse support vector machines J Bi, K Bennett, M Embrechts, C Breneman, M Song Journal of Machine Learning Research 3 (Mar), 1229-1243, 2003 | 629 | 2003 |
| Active learning via transductive experimental design K Yu, J Bi, V Tresp Proceedings of the 23rd international conference on Machine learning, 1081-1088, 2006 | 441 | 2006 |
| Regression error characteristic curves J Bi, KP Bennett Proceedings of the 20th international conference on machine learning (ICML …, 2003 | 404 | 2003 |
| Support vector classification with input data uncertainty J Bi, T Zhang Advances in neural information processing systems 17, 2004 | 318 | 2004 |
| Discrete graph structure learning for forecasting multiple time series C Shang, J Chen, J Bi arXiv preprint arXiv:2101.06861, 2021 | 308 | 2021 |
| A survey on multiview clustering G Chao, S Sun, J Bi IEEE transactions on artificial intelligence 2 (2), 146-168, 2021 | 271 | 2021 |
| Prediction of protein retention times in anion-exchange chromatography systems using support vector regression M Song, CM Breneman, J Bi, N Sukumar, KP Bennett, S Cramer, N Tugcu Journal of chemical information and computer sciences 42 (6), 1347-1357, 2002 | 222 | 2002 |
| Bayesian multiple instance learning: automatic feature selection and inductive transfer VC Raykar, B Krishnapuram, J Bi, M Dundar, RB Rao Proceedings of the 25th international conference on Machine learning, 808-815, 2008 | 190 | 2008 |
| Transcriptional profiles of bovine in vivo pre-implantation development Z Jiang, J Sun, H Dong, O Luo, X Zheng, C Obergfell, Y Tang, J Bi, ... BMC genomics 15, 1-15, 2014 | 187 | 2014 |
| Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults JK Johannesen, J Bi, R Jiang, JG Kenney, CMA Chen Neuropsychiatric electrophysiology 2, 1-21, 2016 | 174 | 2016 |
| A survey on multi-view clustering G Chao, S Sun, J Bi arXiv preprint arXiv:1712.06246, 2017 | 166 | 2017 |
| VIGAN: Missing view imputation with generative adversarial networks C Shang, A Palmer, J Sun, KS Chen, J Lu, J Bi 2017 IEEE International conference on big data (Big Data), 766-775, 2017 | 150 | 2017 |
| Machine-learning-assisted de novo design of organic molecules and polymers: opportunities and challenges G Chen, Z Shen, A Iyer, UF Ghumman, S Tang, J Bi, W Chen, Y Li Polymers 12 (1), 163, 2020 | 145 | 2020 |
| A geometric approach to support vector regression J Bi, KP Bennett Neurocomputing 55 (1-2), 79-108, 2003 | 137 | 2003 |
| Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data AA Farhan, C Yue, R Morillo, S Ware, J Lu, J Bi, J Kamath, A Russell, ... 2016 IEEE wireless health (WH), 1-8, 2016 | 121 | 2016 |
| Column-generation boosting methods for mixture of kernels J Bi, T Zhang, KP Bennett Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004 | 114 | 2004 |
| Edge attention-based multi-relational graph convolutional networks C Shang, Q Liu, KS Chen, J Sun, J Lu, J Yi, J Bi arXiv preprint arXiv:1802.04944 2, 2018 | 109 | 2018 |
| Learning classifiers when the training data is not IID. M Dundar, B Krishnapuram, J Bi, RB Rao IJCAI 2007, 756-61, 2007 | 103 | 2007 |
