My current interests include
Optimization and Game Theory.
Machine Learning.
Optimal Transport.
Large-Scale Text Analytics.
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
Deterministic Nonsmooth Nonconvex Optimization (An earlier version is available here: link)
(α-β order) Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir and Manolis Zampetakis
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. 2023
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
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
On the Global Linear Convergence of the ADMM with Multi-Block Variables
Tianyi Lin, Shiqian Ma and Shuzhong Zhang
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)
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)
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)
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)
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)