Tianyi Lin
Cited by
Cited by
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
On gradient descent ascent for nonconvex-concave minimax problems
T Lin, C Jin, M Jordan
International Conference on Machine Learning, 6083-6093, 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
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
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
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
Near-optimal algorithms for minimax optimization
T Lin, C Jin, MI Jordan
Conference on Learning Theory, 2738-2779, 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
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
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
An extragradient-based alternating direction method for convex minimization
T Lin, S Ma, S Zhang
Foundations of Computational Mathematics 17 (1), 35-59, 2017
Relaxed Wasserstein with applications to GANs
X Guo, J Hong, T Lin, N Yang
ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021
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
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
On the complexity of approximating multimarginal optimal transport
T Lin, N Ho, M Cuturi, MI Jordan
arXiv preprint arXiv:1910.00152, 2019
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
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
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
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
Sparsemax and relaxed Wasserstein for topic sparsity
T Lin, Z Hu, X Guo
Proceedings of the Twelfth ACM International Conference on Web Search and …, 2019
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Articles 1–20