| 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 | 131 | 2015 |
| On gradient descent ascent for nonconvex-concave minimax problems T Lin, C Jin, M Jordan International Conference on Machine Learning, 6083-6093, 2020 | 101 | 2020 |
| 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 | 97 | 2014 |
| 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 | 91* | 2015 |
| 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 | 85 | 2017 |
| 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 | 66 | 2019 |
| 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 | 54 | 2016 |
| Near-optimal algorithms for minimax optimization T Lin, C Jin, MI Jordan Conference on Learning Theory, 2738-2779, 2020 | 52 | 2020 |
| On efficient optimal transport: An analysis of greedy and accelerated mirror descent algorithms T Lin, N Ho, MI Jordan Proceedings of the 36th International Conference on Machine Learning, 3982-3991, 2019 | 50 | 2019 |
| 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 | 43 | 2014 |
| An extragradient-based alternating direction method for convex minimization T Lin, S Ma, S Zhang Foundations of Computational Mathematics 17 (1), 35-59, 2017 | 41 | 2017 |
| Relaxed Wasserstein with applications to GANs X Guo, J Hong, T Lin, N Yang arXiv preprint arXiv:1705.07164, 2017 | 25 | 2017 |
| 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 | 23 | 2018 |
| Adaptively accelerating cubic regularized Newton's methods for convex optimization via random sampling X Chen, B Jiang, T Lin, S Zhang arXiv preprint arXiv:1802.05426, 2018 | 21 | 2018 |
| A unified adaptive tensor approximation scheme to accelerate composite convex optimization B Jiang, T Lin, S Zhang SIAM Journal on Optimization 30 (4), 2897-2926, 2020 | 16* | 2020 |
| On the complexity of approximating multimarginal optimal transport T Lin, N Ho, M Cuturi, MI Jordan arXiv preprint arXiv:1910.00152, 2019 | 16 | 2019 |
| On the efficiency of the Sinkhorn and Greenkhorn algorithms and their acceleration for optimal transport T Lin, N Ho, MI Jordan arXiv preprint arXiv:1906.01437, 2019 | 15* | 2019 |
| Latent aspect mining via exploring sparsity and intrinsic information Y Xu, T Lin, W Lam, Z Zhou, H Cheng, AMC So Proceedings of the 23rd ACM International Conference on Conference on …, 2014 | 12 | 2014 |
| 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 | 11* | 2020 |
| Improved sample complexity for stochastic compositional variance reduced gradient T Lin, C Fan, M Wang, MI Jordan 2020 American Control Conference (ACC), 126-131, 2020 | 10* | 2020 |
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
