Artiklar med krav på offentlig åtkomst - Tianyi LinLäs mer
Tillgängliga någonstans: 35
Near-optimal algorithms for minimax optimization
T Lin, C Jin, MI Jordan
Conference on Learning Theory, 2738-2779, 2020
Krav: US Department of Defense
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
Krav: Research Grants Council, Hong Kong
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
Krav: US National Science Foundation, US Department of Defense
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
Krav: US National Science Foundation, National Natural Science Foundation of China …
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
Krav: US National Science Foundation, US Department of Defense, Research Grants …
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
Krav: Research Grants Council, Hong Kong
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
Krav: National Natural Science Foundation of China, Research Grants Council, Hong Kong
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
Krav: US National Science Foundation, Research Grants Council, Hong Kong
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
Krav: US National Science Foundation, US Department of Defense
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
Krav: US Department of Defense
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
Krav: US National Science Foundation, US Department of Defense
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
Krav: US National Science Foundation
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
Krav: US National Science Foundation, US Department of Defense
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
Krav: US Department of Defense
An extragradient-based alternating direction method for convex minimization
T Lin, S Ma, S Zhang
Foundations of Computational Mathematics 17 (1), 35-59, 2017
Krav: US National Science Foundation, Research Grants Council, Hong Kong
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
Krav: US National Science Foundation
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
Krav: Research Grants Council, Hong Kong
Gradient-free methods for deterministic and stochastic nonsmooth nonconvex optimization
T Lin, Z Zheng, M Jordan
Advances in Neural Information Processing Systems 35, 26160-26175, 2022
Krav: US Department of Defense
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
Krav: US National Science Foundation, National Natural Science Foundation of China
Deterministic nonsmooth nonconvex optimization
M Jordan, G Kornowski, T Lin, O Shamir, M Zampetakis
The Thirty Sixth Annual Conference on Learning Theory, 4570-4597, 2023
Krav: US Department of Defense, UK Engineering and Physical Sciences Research …
Publikations- och finansieringsuppgifter tas fram automatiskt av ett datorprogram.