Tianyi Lin

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 72

Top Topics

Complexity Analysis

Alternating Direction

Convex Optimization

Top Venues

J. Mach. Learn. Res.

Publications

Tianyi Lin, Marco Cuturi, Michael I. Jordan
A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport. AISTATS (2024)
Jun Du, Tianyi Lin, Chunxiao Jiang, Qianqian Yang, Faouzi Bader, Zhu Han
Distributed Foundation Models for Multi-Modal Learning in 6G Wireless Networks. IEEE Wirel. Commun. 31 (3) (2024)
Michael I. Jordan, Tianyi Lin, Manolis Zampetakis
First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems. J. Mach. Learn. Res. 24 (2023)
Michael I. Jordan, Tianyi Lin, Zhengyuan Zhou
Adaptive, Doubly Optimal No-Regret Learning in Strongly Monotone and Exp-Concave Games with Gradient Feedback. CoRR (2023)
Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis
Deterministic Nonsmooth Nonconvex Optimization. CoRR (2023)
Tianyi Lin, Jun Du, Haijun Zhang, Arumugam Nallanathan, Jun Wang
PPO-Based Energy-Efficient Power Control and Spectrum Allocation in In-Vehicle HetNets. GLOBECOM (2023)
Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis
Deterministic Nonsmooth Nonconvex Optimization. COLT (2023)
Tianyi Lin, Marco Cuturi, Michael I. Jordan
A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport. CoRR (2023)
Yang Cai, Michael I. Jordan, Tianyi Lin, Argyris Oikonomou, Emmanouil-Vasileios Vlatakis-Gkaragkounis
Curvature-Independent Last-Iterate Convergence for Games on Riemannian Manifolds. CoRR (2023)
Tianyi Lin, Nhat Ho, Marco Cuturi, Michael I. Jordan
On the Complexity of Approximating Multimarginal Optimal Transport. J. Mach. Learn. Res. 23 (2022)
Michael I. Jordan, Tianyi Lin, Manolis Zampetakis
First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems. CoRR (2022)
Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. ICML (2022)
Tianyi Lin, Michael I. Jordan
A Continuous-Time Perspective on Monotone Equation Problems. CoRR (2022)
Michael I. Jordan, Tianyi Lin, Emmanouil-Vasileios Vlatakis-Gkaragkounis
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces. NeurIPS (2022)
Tianyi Lin, Nhat Ho, Michael I. Jordan
On the Efficiency of Entropic Regularized Algorithms for Optimal Transport. J. Mach. Learn. Res. 23 (2022)
Michael I. Jordan, Tianyi Lin, Manolis Zampetakis
On the Complexity of Deterministic Nonsmooth and Nonconvex Optimization. CoRR (2022)
Michael I. Jordan, Tianyi Lin, Emmanouil-Vasileios Vlatakis-Gkaragkounis
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces. CoRR (2022)
Xi Chen, Bo Jiang, Tianyi Lin, Shuzhong Zhang
Accelerating Adaptive Cubic Regularization of Newton's Method via Random Sampling. J. Mach. Learn. Res. 23 (2022)
Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. CoRR (2022)
Tianyi Lin, Zeyu Zheng, Michael I. Jordan
Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization. NeurIPS (2022)
Leonid Boytsov, Tianyi Lin, Fangwei Gao, Yutian Zhao, Jeffrey Huang, Eric Nyberg
Understanding Performance of Long-Document Ranking Models through Comprehensive Evaluation and Leaderboarding. CoRR (2022)
Yaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael I. Jordan
Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization. AISTATS (2022)
Tianyi Lin, Michael I. Jordan
A control-theoretic perspective on optimal high-order optimization. Math. Program. 195 (1) (2022)
Tianyi Lin, Panayotis Mertikopoulos, Michael I. Jordan
Explicit Second-Order Min-Max Optimization Methods with Optimal Convergence Guarantee. CoRR (2022)
Nhat Ho, Tianyi Lin, Michael I. Jordan
On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms. AISTATS (2022)
Tianyi Lin, Zeyu Zheng, Michael I. Jordan
Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization. CoRR (2022)
Tianyi Lin, Michael I. Jordan
Perseus: A Simple High-Order Regularization Method for Variational Inequalities. CoRR (2022)
Xin Guo, Johnny Hong, Tianyi Lin, Nan Yang
Relaxed Wasserstein with Applications to GANs. ICASSP (2021)
Yaodong Yu, Tianyi Lin, Eric Mazumdar, Michael I. Jordan
Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization. CoRR (2021)
Tianyi Lin, Zeyu Zheng, Elynn Y. Chen, Marco Cuturi, Michael I. Jordan
On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification. AISTATS (2021)
Wenshuo Guo, Michael I. Jordan, Tianyi Lin
A Variational Inequality Approach to Bayesian Regression Games. CoRR (2021)
Tianyi Lin, Zhengyuan Zhou, Wenjia Ba, Jiawei Zhang
Optimal No-Regret Learning in Strongly Monotone Games with Bandit Feedback. CoRR (2021)
Wenshuo Guo, Michael I. Jordan, Tianyi Lin
A Variational Inequality Approach to Bayesian Regression Games. CDC (2021)
Tianyi Lin, Shiqian Ma, Yinyu Ye, Shuzhong Zhang
An ADMM-based interior-point method for large-scale linear programming. Optim. Methods Softw. 36 (2-3) (2021)
Tianyi Lin, Michael I. Jordan
On Monotone Inclusions, Acceleration and Closed-Loop Control. CoRR (2021)
Tianyi Lin, Zeyu Zheng, Elynn Y. Chen, Marco Cuturi, Michael I. Jordan
On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification. CoRR (2020)
Tianyi Lin, Zhengyuan Zhou, Panayotis Mertikopoulos, Michael I. Jordan
Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games. CoRR (2020)
Tianyi Lin, Nhat Ho, Xi Chen, Marco Cuturi, Michael I. Jordan
Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm. NeurIPS (2020)
Tianyi Lin, Chi Jin, Michael I. Jordan
Near-Optimal Algorithms for Minimax Optimization. CoRR (2020)
Tianyi Lin, Zhengyuan Zhou, Panayotis Mertikopoulos, Michael I. Jordan
Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games. ICML (2020)
Tianyi Lin, Nhat Ho, Xi Chen, Marco Cuturi, Michael I. Jordan
Revisiting Fixed Support Wasserstein Barycenter: Computational Hardness and Efficient Algorithms. CoRR (2020)
Tianyi Lin, Chenyou Fan, Nhat Ho, Marco Cuturi, Michael I. Jordan
Projection Robust Wasserstein Distance and Riemannian Optimization. CoRR (2020)
Tianyi Lin, Chengyou Fan, Mengdi Wang, Michael I. Jordan
Improved Sample Complexity for Stochastic Compositional Variance Reduced Gradient. ACC (2020)
Tianyi Lin, Chenyou Fan, Nhat Ho, Marco Cuturi, Michael I. Jordan
Projection Robust Wasserstein Distance and Riemannian Optimization. NeurIPS (2020)
Bo Jiang, Tianyi Lin, Shuzhong Zhang
A Unified Adaptive Tensor Approximation Scheme to Accelerate Composite Convex Optimization. SIAM J. Optim. 30 (4) (2020)
Ilan Adler, Zhiyue Tom Hu, Tianyi Lin
New Proximal Newton-Type Methods for Convex Optimization. CDC (2020)
Tianyi Lin, Chi Jin, Michael I. Jordan
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. ICML (2020)
Tianyi Lin, Chi Jin, Michael I. Jordan
Near-Optimal Algorithms for Minimax Optimization. COLT (2020)
Tianyi Lin, Nhat Ho, Marco Cuturi, Michael I. Jordan
On the Complexity of Approximating Multimarginal Optimal Transport. CoRR (2019)
Bo Jiang, Tianyi Lin, Shiqian Ma, Shuzhong Zhang
Structured nonconvex and nonsmooth optimization: algorithms and iteration complexity analysis. Comput. Optim. Appl. 72 (1) (2019)
Tianyi Lin, Michael I. Jordan
A Control-Theoretic Perspective on Optimal High-Order Optimization. CoRR (2019)
Nhat Ho, Tianyi Lin, Michael I. Jordan
-Convex Clustering. CoRR (2019)
Tianyi Lin, Nhat Ho, Michael I. Jordan
On the Acceleration of the Sinkhorn and Greenkhorn Algorithms for Optimal Transport. CoRR (2019)
Tianyi Lin, Nhat Ho, Michael I. Jordan
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms. ICML (2019)
Tianyi Lin, Zhiyue Hu, Xin Guo
Sparsemax and Relaxed Wasserstein for Topic Sparsity. WSDM (2019)
Tianyi Lin, Nhat Ho, Michael I. Jordan
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms. CoRR (2019)
Tianyi Lin, Chi Jin, Michael I. Jordan
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. CoRR (2019)
Tianyi Lin, Shiqian Ma, Shuzhong Zhang
Global Convergence of Unmodified 3-Block ADMM for a Class of Convex Minimization Problems. J. Sci. Comput. 76 (1) (2018)
Linbo Qiao, Tianyi Lin, Qi Qin, Xicheng Lu
On the Iteration Complexity Analysis of Stochastic Primal-Dual Hybrid Gradient Approach with High Probability. CoRR (2018)
Necdet Serhat Aybat, Zi Wang, Tianyi Lin, Shiqian Ma
Distributed Linearized Alternating Direction Method of Multipliers for Composite Convex Consensus Optimization. IEEE Trans. Autom. Control. 63 (1) (2018)
Tianyi Lin, Chenyou Fan, Mengdi Wang, Michael I. Jordan
Improved Oracle Complexity for Stochastic Compositional Variance Reduced Gradient. CoRR (2018)
Tianyi Lin, Linbo Qiao, Teng Zhang, Jiashi Feng, Bofeng Zhang
Stochastic Primal-Dual Proximal ExtraGradient descent for compositely regularized optimization. Neurocomputing 273 (2018)
Linbo Qiao, Tianyi Lin, Qi Qin, Xicheng Lu
On the iteration complexity analysis of Stochastic Primal-Dual Hybrid Gradient approach with high probability. Neurocomputing 307 (2018)
Tianyi Lin, Zhiyue Hu, Xin Guo
Sparsemax and Relaxed Wasserstein for Topic Sparsity. CoRR (2018)
Yinqing Xu, Qian Yu, Wai Lam, Tianyi Lin
Exploiting interactions of review text, hidden user communities and item groups, and time for collaborative filtering. Knowl. Inf. Syst. 52 (1) (2017)
Tianyi Lin, Shiqian Ma, Shuzhong Zhang
An Extragradient-Based Alternating Direction Method for Convex Minimization. Found. Comput. Math. 17 (1) (2017)
Xin Guo, Johnny Hong, Tianyi Lin, Nan Yang
Relaxed Wasserstein with Applications to GANs. CoRR (2017)
Tianyi Lin, Linbo Qiao, Teng Zhang, Jiashi Feng, Bofeng Zhang
Stochastic Primal-Dual Proximal ExtraGradient descent for compositely regularized optimization. CoRR (2017)
Linbo Qiao, Tianyi Lin, Yu-Gang Jiang, Fan Yang, Wei Liu, Xicheng Lu
On Stochastic Primal-Dual Hybrid Gradient Approach for Compositely Regularized Minimization. ECAI (2016)
Tianyi Lin, Shiqian Ma, Shuzhong Zhang
Iteration Complexity Analysis of Multi-block ADMM for a Family of Convex Minimization Without Strong Convexity. J. Sci. Comput. 69 (1) (2016)
Tianyi Lin, Siyuan Zhang, Hong Cheng
Understanding Sparse Topical Structure of Short Text via Stochastic Variational-Gibbs Inference. CIKM (2016)
Bo Jiang, Tianyi Lin, Shiqian Ma, Shuzhong Zhang
Structured Nonconvex and Nonsmooth Optimization: Algorithms and Iteration Complexity Analysis. CoRR (2016)
Tianyi Lin, Shiqian Ma, Shuzhong Zhang
Global Convergence of Unmodified 3-Block ADMM for a Class of Convex Minimization Problems. CoRR (2015)
Tianyi Lin, Shiqian Ma, Shuzhong Zhang
On the Global Linear Convergence of the ADMM with MultiBlock Variables. SIAM J. Optim. 25 (3) (2015)
Tianyi Lin, Wentao Tian, Qiaozhu Mei, Hong Cheng
The dual-sparse topic model: mining focused topics and focused terms in short text. WWW (2014)
Yinqing Xu, Wai Lam, Tianyi Lin
Collaborative Filtering Incorporating Review Text and Co-clusters of Hidden User Communities and Item Groups. CIKM (2014)
Yinqing Xu, Tianyi Lin, Wai Lam, Zirui Zhou, Hong Cheng, Anthony Man-Cho So
Latent Aspect Mining via Exploring Sparsity and Intrinsic Information. CIKM (2014)