Large-scale linear ranksvm CP Lee, CJ Lin
Neural computation 26 (4), 781-817, 2014
158 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
122 2014 A study on L2-loss (squared hinge-loss) multiclass SVM CP Lee, CJ Lin
Neural computation 25 (5), 1302-1323, 2013
91 2013 A revisit to support vector data description WC Chang, CP Lee, CJ Lin
Dept. Comput. Sci., Nat. Taiwan Univ., Taipei, Taiwan, Tech. Rep, 2013
89 2013 Distributed box-constrained quadratic optimization for dual linear SVM CP Lee, D Roth
International Conference on Machine Learning, 987-996, 2015
67 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
55 2019 Inexact successive quadratic approximation for regularized optimization C Lee, SJ Wright
Computational Optimization and Applications 72, 641-674, 2019
53 2019 Large-scale kernel ranksvm TM Kuo, CP Lee, CJ Lin
Proceedings of the 2014 SIAM international conference on data mining, 812-820, 2014
50 2014 Analyzing random permutations for cyclic coordinate descent S Wright, C Lee
Mathematics of Computation 89, 2217-2248, 2020
35 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
31 2018 Accelerating Inexact Successive Quadratic Approximation for Regularized Optimization Through Manifold Identification C Lee
Mathematical Programming, 2023
17 2023 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 First-Order Algorithms Converge Faster than CP Lee, S Wright
International Conference on Machine Learning, 3754-3762, 2019
14 2019 Distributed block-diagonal approximation methods for regularized empirical risk minimization C Lee, KW Chang
Machine Learning 109 (4), 813-852, 2020
13 2020 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
12 2019 Training Structured Neural Networks Through Manifold Identification and Variance Reduction ZS Huang, C Lee
The 10th International Conference on Learning Representations, 2021
11 2021 Distributed training of structured SVM C Lee, KW Chang, S Upadhyay, D Roth
NIPS Workshop on Optimization for Machine Learning, 2015
9 2015 Accelerating nuclear-norm regularized low-rank matrix optimization through Burer-Monteiro decomposition C Lee, L Liang, T Tang, KC Toh
Journal of Machine Learning Research 25, 1-52, 2024
8 * 2024 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 
Chih-Jen LinDistinguished professor, National Taiwan University and affiliated professor, MBZUAIEmail verificata su csie.ntu.edu.tw
