My current interests include
Optimization.
Machine Learning.
Operations Research.
Game Theory and Applications.
Optimal Transport.
Perseus: A Simple and Optimal High-Order Method for Variational Inequalities
Tianyi Lin and Michael I. Jordan
A Nonasymptotic Analysis of Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
Tianyi Lin, Chi Jin and Michael I. Jordan
Adaptive, Doubly Optimal No-Regret Learning in Games with Gradient Feedback
(α-β order) Michael I. Jordan, Tianyi Lin and Zhengyuan Zhou
Explicit Second-Order Min-Max Optimization Methods with Optimal Convergence Guarantee
Tianyi Lin, Panayotis Mertikopoulos and Michael. I. Jordan
On the Complexity of Deterministic Nonsmooth and Nonconvex Optimization
(α-β order) Michael I. Jordan, Tianyi Lin and Manolis Zampetakis
First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems
A Continuous-Time Perspective on Optimal Methods for Monotone Equation Problems
Doubly Optimal No-Regret Online Learning in Strongly Monotone Games with Bandit Feedback
Tianyi Lin, Zhengyuan Zhou, Wenjia Ba and Jiawei Zhang
Monotone Inclusions, Acceleration and Closed-Loop Control
Mathematics of Operations Research. Forthcoming. 2022
A Control-Theoretic Perspective on Optimal High-Order Optimization
Mathematical Programming (Series A). 195 (1): 929-975. 2022
On the Efficiency of Entropic Regularized Algorithms for Optimal Transport
Tianyi Lin, Nhat Ho and Michael I. Jordan
Journal of Machine Learning Research. 23 (137): 1-42. 2022
Accelerating Adaptive Cubic Regularization of Newton's Method via Random Sampling
(α-β order) Xi Chen, Bo Jiang, Tianyi Lin and Shuzhong Zhang
Journal of Machine Learning Research. 23 (90): 1-38. 2022
On the Complexity of Approximating Multimarginal Optimal Transport
Tianyi Lin*, Nhat Ho*, Macro Cuturi and Michael I. Jordan
Journal of Machine Learning Research. 23 (65): 1-43. 2022
An ADMM-Based Interior-Point Method for Large-Scale Linear Programming
Tianyi Lin, Shiqian Ma, Yinyu Ye and Shuzhong Zhang
Optimization Methods and Software. 36 (2-3): 389-424. 2021
A Unified Adaptive Tensor Approximation Scheme to Accelerate Composite Convex Optimization
(α-β order) Bo Jiang, Tianyi Lin and Shuzhong Zhang
SIAM Journal on Optimization. 30 (4): 2897-2926. 2020
Structured Nonconvex Optimization Models: Algorithms and Iteration Complexity Analysis
(α-β order) Bo Jiang, Tianyi Lin, Shiqian Ma and Shuzhong 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
Linbo Qiao, Tianyi Lin, Qi Qin and Xicheng Lu
Neurocomputing. 307: 78-90. 2018
Global Convergence of Unmodified 3-Block ADMM for a Class of Convex Minimization Problems
Tianyi Lin, Shiqian Ma and Shuzhong Zhang
Journal of Scientific Computing. 76 (1): 69-88. 2018
Stochastic Primal-Dual Proximal ExtraGradient Descent for Compositely Regularized Optimization
Tianyi Lin, Linbo Qiao, Teng Zhang, Jiashi Feng and Bofeng Zhang
Neurocomputing. 273: 516-525. 2018
Distributed Linearized Alternating Direction Method of Multipliers for Composite Convex Consensus Optimization
Necdet S. Aybat, Zi Wang, Tianyi Lin and Shiqian 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
Yinqing Xu, Qian Yu, Wai Lam and Tianyi 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
Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization
Tianyi Lin, Zeyu Zheng and Michael I. Jordan
Neural Information Processing Systems (NeurIPS’2022)
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces
(α-β order) Michael I. Jordan, Tianyi Lin and Emmanouil-Vasileios Vlatakis-Gkaragkounis
(Oral) Neural Information Processing Systems (NeurIPS’2022)
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback
Tianyi Lin*, Aldo Pacchiano*, Yaodong Yu*, Michael I. Jordan
International Conference on Machine Learning (ICML’2022)
Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization
Yaodong Yu*, Tianyi Lin*, Eric Mazumdar*, Michael I. Jordan
Artificial Intelligence and Statistics Conference (AISTATS’2022)
On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms
Nhat Ho*, Tianyi Lin* and Michael I. Jordan
A Variational Inequality Approach to Bayesian Regression Games
(α-β order) Wenshuo Guo, Michael I. Jordan and Tianyi Lin
IEEE Conference on Decision and Control (CDC’2021)
On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification
Tianyi Lin, Zeyu Zheng, Elynn Y. Chen, Marco Cuturi and Michael I. Jordan
Artificial Intelligence and Statistics Conference (AISTATS’2021)
Relaxed Wasserstein with Applications to GANs
(α-β order) Xin Guo, Johnny Hong, Tianyi Lin and Nan Yang
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’2021)
Projection Robust Wasserstein Distance and Riemannian Optimization
Tianyi Lin*, Chenyou Fan*, Nhat Ho, Macro Cuturi and Michael I. Jordan
(Spotlight) Neural Information Processing Systems (NeurIPS’2020)
Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm
Tianyi Lin, Nhat Ho, Xi Chen, Macro Cuturi and Michael I. Jordan
Neural Information Processing Systems (NeurIPS’2020)
New Proximal Newton-Type Methods for Convex Optimization
(α-β order) Ilan Adler, Zhiyue T. Hu and Tianyi Lin
IEEE Conference on Decision and Control (CDC’2020)
Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games
Tianyi Lin*, Zhengyuan Zhou*, Panayotis Mertikopoulos and Michael I. Jordan
International Conference on Machine Learning (ICML’2020)
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
Near-Optimal Algorithms for Minimax Optimization
Conference on Learning Theory (COLT’2020)
Improved Sample Complexity for Stochastic Compositional Variance Reduced Gradient
Tianyi Lin, Chenyou Fan, Mengdi Wang and Michael I. Jordan
American Control Conference (ACC’2020)
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms
Tianyi Lin*, Nhat Ho* and Michael I. Jordan
International Conference on Machine Learning (ICML’2019)
Sparsemax and Relaxed Wasserstein for Topic Sparsity
Tianyi Lin, Zhiyue T. Hu and Xin Guo
ACM International Conference on Web Search and Data Mining (WSDM’2019)
Understanding Sparse Topical Structure of Short Text via Stochastic Variational-Gibbs Inference
Tianyi Lin, Siyuan Zhang and Hong Cheng
ACM Conference on Information and Knowledge Management (CIKM’2016)
On Stochastic Primal-Dual Hybrid Gradient Approach for Compositely Regularized Minimization
Linbo Qiao, Tianyi Lin, Yugang Jiang, Fan Yang, Wei Liu and Xicheng Lu
European Conference on Artificial Intelligence (ECAI’2016)
Collaborative Filtering Incorporating Review Text and Co-clusters of Hidden User Communities and Item Groups
Yinqing Xu, Wai Lam and Tianyi Lin
ACM Conference on Information and Knowledge Management (CIKM’2014)
Latent Aspect Mining via Exploring Sparsity and Intrinsic Information
Yinqing Xu, Tianyi Lin, Wai Lam, Zirui Zhou, Hong Cheng and Anthony So,
The Dual-Sparse Topic Model: Mining Focused Topics and Focused Terms in Short Text
Tianyi Lin*, Wentao Tian*, Qiaozhu Mei and Hong Cheng
International World Wide Web Conference (WWW’2014)