| Large-scale linear ranksvm CP Lee, CJ Lin Neural computation 26 (4), 781-817, 2014 | 155 | 2014 |
| Large-scale logistic regression and linear support vector machines using spark CY Lin, CH Tsai, CP Lee, CJ Lin 2014 IEEE International Conference on Big Data (Big Data), 519-528, 2014 | 118 | 2014 |
| A revisit to support vector data description WC Chang, CP Lee, CJ Lin Dept. Comput. Sci., Nat. Taiwan Univ., Taipei, Taiwan, Tech. Rep, 2013 | 87 | 2013 |
| A study on L2-loss (squared hinge-loss) multiclass SVM CP Lee, CJ Lin Neural computation 25 (5), 1302-1323, 2013 | 85 | 2013 |
| Distributed box-constrained quadratic optimization for dual linear SVM CP Lee, D Roth International Conference on Machine Learning, 987-996, 2015 | 65 | 2015 |
| Random permutations fix a worst case for cyclic coordinate descent CP Lee, SJ Wright IMA Journal of Numerical Analysis 39 (3), 1246-1275, 2019 | 50 | 2019 |
| Large-scale kernel ranksvm TM Kuo, CP Lee, CJ Lin Proceedings of the 2014 SIAM international conference on data mining, 812-820, 2014 | 48 | 2014 |
| Inexact successive quadratic approximation for regularized optimization C Lee, SJ Wright Computational Optimization and Applications 72, 641-674, 2019 | 46 | 2019 |
| Analyzing random permutations for cyclic coordinate descent S Wright, C Lee Mathematics of Computation 89, 2217-2248, 2020 | 31 | 2020 |
| A distributed quasi-Newton algorithm for empirical risk minimization with nonsmooth regularization C Lee, CH Lim, SJ Wright Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018 | 30 | 2018 |
| Limited-memory common-directions method for distributed optimization and its application on empirical risk minimization CP Lee, PW Wang, W Chen, CJ Lin Proceedings of the 2017 SIAM International Conference on Data Mining, 732-740, 2017 | 16 | 2017 |
| Accelerating Inexact Successive Quadratic Approximation for Regularized Optimization Through Manifold Identification C Lee mathematical Programming, 2023 | 15 | 2023 |
| Distributed block-diagonal approximation methods for regularized empirical risk minimization C Lee, KW Chang Machine Learning 109 (4), 813-852, 2020 | 12 | 2020 |
| First-Order Algorithms Converge Faster than on Convex Problems CP Lee, S Wright International Conference on Machine Learning, 3754-3762, 2019 | 12 | 2019 |
| The common-directions method for regularized empirical risk minimization PW Wang, C Lee, CJ Lin The Journal of Machine Learning Research 20 (1), 2072-2120, 2019 | 11 | 2019 |
| Distributed training of structured SVM C Lee, KW Chang, S Upadhyay, D Roth NIPS Workshop on Optimization for Machine Learning, 2015 | 9 | 2015 |
| Training Structured Neural Networks Through Manifold Identification and Variance Reduction ZS Huang, C Lee The 10th International Conference on Learning Representations, 2021 | 8 | 2021 |
| Using neural networks to detect line outages from PMU data C Lee, SJ Wright arXiv preprint arXiv:1710.05916, 2017 | 8 | 2017 |
| Limited-memory Common-directions Method for Large-scale Optimization: Convergence, Parallelization, and Distributed Optimization C Lee, PW Wang, CJ Lin Mathematical Programming Computation, 2022 | 5 | 2022 |
| Manifold Identification for Ultimately Communication-Efficient Distributed Optimization YS Li, WL Chiang, C Lee International Conference on Machine Learning, 2020 | 5 | 2020 |
Chih-Jen LinDistinguished professor, National Taiwan University and affiliated professor, MBZUAI在 csie.ntu.edu.tw 的电子邮件经过验证
