Research

Research Interests

My current interests include

• Algorithmic Design and Complexity Analysis.

• Statistical and Computational Optimal Transport.

• Multi-Agent Learning in Games.

• Text Mining.

Preprints

• Perseus: A Simple High-Order Regularization Method for Variational Inequalities

  • Tianyi Lin and Michael I. Jordan

• A Continuous-Time Perspective on Monotone Equation Problems

  • Tianyi Lin and Michael I. Jordan

• First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces

  • (α-β order) Michael I. Jordan, Tianyi Lin and Emmanouil-Vasileios Vlatakis-Gkaragkounis

• First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems

  • (α-β order) Michael I. Jordan, Tianyi Lin and Manolis Zampetakis

• Doubly Optimal No-Regret Online Learning with Bandit Feedback

  • Tianyi Lin, Zhengyuan Zhou, Wenjia Ba and Jiawei Zhang

• Monotone Inclusions, Acceleration and Closed-Loop Control

  • Tianyi Lin and Michael I. Jordan

Refereed Journal Publications

• 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.

• A Control-Theoretic Perspective on Optimal High-Order Optimization

  • Tianyi Lin and Michael I. Jordan

  • Mathematical Programming Series A. To appear. 2021

• 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

  • Tianyi Lin, Shiqian Ma and Shuzhong Zhang

  • 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

  • Tianyi Lin, Shiqian Ma and Shuzhong Zhang

  • Journal of Scientific Computing. 69: 52-81. 2016

• On the Sublinear Convergence Rate of Multi-Block ADMM

  • Tianyi Lin, Shiqian Ma and Shuzhong Zhang

  • 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

  • Tianyi Lin, Shiqian Ma and Shuzhong Zhang

  • SIAM Journal on Optimization. 25 (3): 1478-1497. 2015

Refereed Proceedings

• 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

  • Artificial Intelligence and Statistics Conference (AISTATS’2022)

• 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

  • Tianyi Lin, Chi Jin and Michael I. Jordan

  • International Conference on Machine Learning (ICML’2020)

• Near-Optimal Algorithms for Minimax Optimization

  • Tianyi Lin, Chi Jin and Michael I. Jordan

  • 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,

  • ACM Conference on Information and Knowledge Management (CIKM’2014)

• 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)