My research interests include topics in minmax optimization, computational optimal transport and data mining.
Structured Nonconvex Optimization Models: Algorithms and Iteration Complexity Analysis
B. Jiang, T. Lin, S. Ma and S. Zhang
Computational Optimization and Applications, 72 (1): 115-157, 2019
On the Iteration Complexity Analysis of Stochastic Primal-Dual Hybrid Gradient approach with High Probability
L. Qiao, T. Lin, Q. Qin and X. Lu
Neurocomputing, 307: 78-90, 2018
Global Convergence of Unmodified 3-Block ADMM for a Class of Convex Minimization Problems
T. Lin, S. Ma and S. Zhang
Journal of Scientific Computing, 76 (1): 69-88, 2018
Stochastic Primal-Dual Proximal ExtraGradient Descent for Compositely Regularized Optimization
T. Lin, L. Qiao, T. Zhang, J. Feng and B. Zhang
Neurocomputing, 273: 516-525, 2018
Distributed Linearized Alternating Direction Method of Multipliers for Composite Convex Consensus Optimization
N. S. Aybat, Z. Wang, T. Lin and S. Ma
IEEE Transactions on Automatic Control, 63 (1): 5-20, 2018
An Extragradient-Based Alternating Direction Method for Convex Minimization
Foundations of Computational Mathematics, 17 (1): 35-59, 2017
Exploiting Interactions of Review Text, Hidden User Communities and Item Groups, and Time for Collaborative Filtering
Y. Xu, Q. Yu, W. Lam and T. Lin
Knowledge and Information Systems, 52 (1): 221-254, 2017
Iteration Complexity Analysis of Multi-Block ADMM for a Family of Convex Minimization without Strong Convexity
Journal of Scientific Computing, 69: 52-81, 2016
On the Sublinear Convergence Rate of Multi-Block ADMM
Journal of the Operations Research Society of China, 3(3): 251-274, 2015
On the Global Linear Convergence of the ADMM with Multi-Block Variables
SIAM Journal on Optimization, 25 (3): 1478-1497, 2015
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms
T. Lin, N. Ho and M. I. Jordan
ICML 2019, Long Beach, USA
Sparsemax and Relaxed Wasserstein for Topic Sparsity
T. Lin, Z. T. Hu and X. Guo
WSDM 2019, Melbourne, Australia
Understanding Sparse Topical Structure of Short Text via Stochastic Variational-Gibbs Inference
T. Lin, S. Zhang and H. Cheng
CIKM 2016, Indianapolis, USA
On Stochastic Primal-Dual Hybrid Gradient Approach for Compositely Regularized Minimization
L. Qiao, T. Lin, Y. Jiang, F. Yang, W. Liu and X. Lu
ECAI 2016, the Hague, Netherlands
Collaborative Filtering Incorporating Review Text and Co-clusters of Hidden User Communities and Item Groups
Y. Xu, W. Lam and T. Lin
CIKM 2014, Shanghai, China
Latent Aspect Mining via Exploring Sparsity and Intrinsic Information
Y. Xu, T. Lin, W. Lam, Z. Zhou, H. Cheng and A. M-C. So,
The Dual-Sparse Topic Model: Mining Focused Topics and Focused Terms in Short Text
T. Lin, W. Tian, Q. Mei and H. Cheng
WWW 2014, Seoul, Korea
On the Complexity of Approximating Multimarginal Optimal Transport
T. Lin, N. Ho, M. Cuturi and M. I. Jordan
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
T. Lin, C. Jin and M. I. Jordan
On the Acceleration of the Sinkhorn and Greenkhorn Algorithms for Optimal Transport
On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms
N. Ho, T. Lin and M. I. Jordan
A Unified Adaptive Tensor Approximation Scheme to Accelerate Composite Convex Optimization
B. Jiang, T. Lin and S. Zhang
Improved Sample Complexity for Stochastic Compositional Variance Reduced Gradient
T. Lin, C. Fan, M. Wang and M. I. Jordan
An ADMM-Based Interior-Point Method for Large-Scale Linear Programming
T. Lin, S. Ma, Y. Ye and S. Zhang
Adaptively Accelerating Cubic Regularized Newton's Methods for Convex Optimization via Random Sampling
X. Chen, B. Jiang, T. Lin and S. Zhang