| On gradient descent ascent for nonconvex-concave minimax problems T Lin, C Jin, M Jordan International Conference on Machine Learning, 6083-6093, 2020 | 508 | 2020 |
| Near-optimal algorithms for minimax optimization T Lin, C Jin, MI Jordan Conference on Learning Theory, 2738-2779, 2020 | 257 | 2020 |
| On the global linear convergence of the ADMM with multiblock variables T Lin, S Ma, S Zhang SIAM Journal on Optimization 25 (3), 1478-1497, 2015 | 201 | 2015 |
| The dual-sparse topic model: mining focused topics and focused terms in short text T Lin, W Tian, Q Mei, H Cheng Proceedings of the 23rd International Conference on World Wide Web, 539-550, 2014 | 154 | 2014 |
| Structured nonconvex and nonsmooth optimization: algorithms and iteration complexity analysis B Jiang, T Lin, S Ma, S Zhang Computational Optimization and Applications 72 (1), 115-157, 2019 | 152 | 2019 |
| Distributed linearized alternating direction method of multipliers for composite convex consensus optimization NS Aybat, Z Wang, T Lin, S Ma IEEE Transactions on Automatic Control 63 (1), 5-20, 2017 | 146 | 2017 |
| On efficient optimal transport: An analysis of greedy and accelerated mirror descent algorithms T Lin, N Ho, M Jordan International Conference on Machine Learning, 3982-3991, 2019 | 138 | 2019 |
| On the sublinear convergence rate of multi-block ADMM TY Lin, SQ Ma, SZ Zhang Journal of the Operations Research Society of China 3 (3), 251-274, 2015 | 133* | 2015 |
| Iteration complexity analysis of multi-block ADMM for a family of convex minimization without strong convexity T Lin, S Ma, S Zhang Journal of Scientific Computing 69 (1), 52-81, 2016 | 84 | 2016 |
| On the complexity of approximating multimarginal optimal transport T Lin, N Ho, M Cuturi, MI Jordan Journal of Machine Learning Research 23 (65), 1-43, 2022 | 71 | 2022 |
| Fixed-support Wasserstein barycenters: Computational hardness and fast algorithm T Lin, N Ho, X Chen, M Cuturi, M Jordan Advances in Neural Information Processing Systems 33, 2020 | 65* | 2020 |
| Projection robust Wasserstein distance and Riemannian optimization T Lin, C Fan, N Ho, M Cuturi, M Jordan Advances in Neural Information Processing Systems 33, 2020 | 62 | 2020 |
| On the efficiency of entropic regularized algorithms for optimal transport T Lin, N Ho, MI Jordan Journal of Machine Learning Research 23 (137), 1-42, 2022 | 59* | 2022 |
| On projection robust optimal transport: Sample complexity and model misspecification T Lin, Z Zheng, E Chen, M Cuturi, M Jordan International Conference on Artificial Intelligence and Statistics, 262-270, 2021 | 57 | 2021 |
| An extragradient-based alternating direction method for convex minimization T Lin, S Ma, S Zhang Foundations of Computational Mathematics 17 (1), 35-59, 2017 | 54 | 2017 |
| An ADMM-based interior-point method for large-scale linear programming T Lin, S Ma, Y Ye, S Zhang Optimization Methods and Software 36 (2-3), 389-424, 2021 | 51 | 2021 |
| Finite-time last-iterate convergence for multi-agent learning in games T Lin, Z Zhou, P Mertikopoulos, MI Jordan International Conference on Machine Learning, 6161-6171, 2020 | 51 | 2020 |
| Collaborative filtering incorporating review text and co-clusters of hidden user communities and item groups Y Xu, W Lam, T Lin Proceedings of the 23rd ACM International Conference on Conference on …, 2014 | 50 | 2014 |
| Global convergence of unmodified 3-block ADMM for a class of convex minimization problems T Lin, S Ma, S Zhang Journal of Scientific Computing 76 (1), 69-88, 2018 | 49 | 2018 |
| Relaxed Wasserstein with applications to GANs X Guo, J Hong, T Lin, N Yang IEEE International Conference on Acoustics, Speech and Signal Processing …, 2021 | 44 | 2021 |
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyEmail được xác minh tại cs.berkeley.edu
