Collaborative Filtering Incorporating Review Text and Co-clusters of Hidden User Communities and Item Groups. - Yinqing Xu; Wai Lam; Tianyi Lin (CIKM, 2014)

Latent Aspect Mining via Exploring Sparsity and Intrinsic Information. - Yinqing Xu; Tianyi Lin; Wai Lam; Zirui Zhou; Hong Cheng 001; Anthony Man-Cho So (CIKM, 2014)

The dual-sparse topic model: mining focused topics and focused terms in short text. - Tianyi Lin; Wentao Tian; Qiaozhu Mei; Hong Cheng 001 (WWW, 2014)

On the Global Linear Convergence of the ADMM with MultiBlock Variables. - Tianyi Lin; Shiqian Ma; Shuzhong Zhang (SIAM J. Optim., 2015)

Understanding Sparse Topical Structure of Short Text via Stochastic Variational-Gibbs Inference. - Tianyi Lin; Siyuan Zhang; Hong Cheng 001 (CIKM, 2016)

On Stochastic Primal-Dual Hybrid Gradient Approach for Compositely Regularized Minimization. - Linbo Qiao; Tianyi Lin; Yu-Gang Jiang 001; Fan Yang; Wei Liu 005; Xicheng Lu (ECAI, 2016)

Iteration Complexity Analysis of Multi-block ADMM for a Family of Convex Minimization Without Strong Convexity. - Tianyi Lin; Shiqian Ma; Shuzhong Zhang (J. Sci. Comput., 2016)

An Extragradient-Based Alternating Direction Method for Convex Minimization. - Tianyi Lin; Shiqian Ma; Shuzhong Zhang (Found. Comput. Math., 2017)

Exploiting interactions of review text, hidden user communities and item groups, and time for collaborative filtering. - Yinqing Xu; Qian Yu; Wai Lam; Tianyi Lin (Knowl. Inf. Syst., 2017)

Improved Oracle Complexity for Stochastic Compositional Variance Reduced Gradient. - Tianyi Lin; Chenyou Fan; Mengdi Wang; Michael I. Jordan (CoRR, 2018)

Stochastic Primal-Dual Proximal ExtraGradient descent for compositely regularized optimization. - Tianyi Lin; Linbo Qiao; Teng Zhang; Jiashi Feng; Bofeng Zhang (Neurocomputing, 2018)

On the iteration complexity analysis of Stochastic Primal-Dual Hybrid Gradient approach with high probability. - Linbo Qiao; Tianyi Lin; Qi Qin; Xicheng Lu (Neurocomputing, 2018)

Global Convergence of Unmodified 3-Block ADMM for a Class of Convex Minimization Problems. - Tianyi Lin; Shiqian Ma; Shuzhong Zhang (J. Sci. Comput., 2018)

Distributed Linearized Alternating Direction Method of Multipliers for Composite Convex Consensus Optimization. - Necdet Serhat Aybat; Zi Wang 007; Tianyi Lin; Shiqian Ma (IEEE Trans. Autom. Control., 2018)

On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms. - Tianyi Lin; Nhat Ho; Michael I. Jordan (ICML, 2019)

Sparsemax and Relaxed Wasserstein for Topic Sparsity. - Tianyi Lin; Zhiyue Hu; Xin Guo 001 (WSDM, 2019)

Structured nonconvex and nonsmooth optimization: algorithms and iteration complexity analysis. - Bo Jiang 007; Tianyi Lin; Shiqian Ma; Shuzhong Zhang (Comput. Optim. Appl., 2019)

Global Error Bounds and Linear Convergence for Gradient-Based Algorithms for Trend Filtering and 𝓁1-Convex Clustering. - Nhat Ho; Tianyi Lin; Michael I. Jordan (CoRR, 2019)

On the Acceleration of the Sinkhorn and Greenkhorn Algorithms for Optimal Transport. - Tianyi Lin; Nhat Ho; Michael I. Jordan (CoRR, 2019)

Improved Sample Complexity for Stochastic Compositional Variance Reduced Gradient. - Tianyi Lin; Chengyou Fan; Mengdi Wang; Michael I. Jordan (ACC, 2020)

New Proximal Newton-Type Methods for Convex Optimization. - Ilan Adler; Zhiyue Tom Hu; Tianyi Lin (CDC, 2020)

Near-Optimal Algorithms for Minimax Optimization. - Tianyi Lin; Chi Jin 001; Michael I. Jordan (COLT, 2020)

On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. - Tianyi Lin; Chi Jin 001; Michael I. Jordan (ICML, 2020)

Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games. - Tianyi Lin; Zhengyuan Zhou; Panayotis Mertikopoulos; Michael I. Jordan (ICML, 2020)

Projection Robust Wasserstein Distance and Riemannian Optimization. - Tianyi Lin; Chenyou Fan; Nhat Ho; Marco Cuturi; Michael I. Jordan (NeurIPS, 2020)

Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm. - Tianyi Lin; Nhat Ho; Xi Chen; Marco Cuturi; Michael I. Jordan (NeurIPS, 2020)

Revisiting Fixed Support Wasserstein Barycenter: Computational Hardness and Efficient Algorithms. - Tianyi Lin; Nhat Ho; Xi Chen; Marco Cuturi; Michael I. Jordan (CoRR, 2020)

A Unified Adaptive Tensor Approximation Scheme to Accelerate Composite Convex Optimization. - Bo Jiang 007; Tianyi Lin; Shuzhong Zhang (SIAM J. Optim., 2020)

On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification. - Tianyi Lin; Zeyu Zheng; Elynn Y. Chen; Marco Cuturi; Michael I. Jordan (AISTATS, 2021)

A Variational Inequality Approach to Bayesian Regression Games. - Wenshuo Guo; Michael I. Jordan; Tianyi Lin (CDC, 2021)

Relaxed Wasserstein with Applications to GANs. - Xin Guo 001; Johnny Hong; Tianyi Lin; Nan Yang (ICASSP, 2021)

Optimal No-Regret Learning in Strongly Monotone Games with Bandit Feedback. - Tianyi Lin; Zhengyuan Zhou; Wenjia Ba; Jiawei Zhang (CoRR, 2021)

An ADMM-based interior-point method for large-scale linear programming. - Tianyi Lin; Shiqian Ma; Yinyu Ye 001; Shuzhong Zhang (Optim. Methods Softw., 2021)

On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms. - Nhat Ho; Tianyi Lin; Michael I. Jordan (AISTATS, 2022)

Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization. - Yaodong Yu; Tianyi Lin; Eric V. Mazumdar; Michael I. Jordan (AISTATS, 2022)

Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. - Tianyi Lin; Aldo Pacchiano; Yaodong Yu; Michael I. Jordan (ICML, 2022)

First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces. - Michael I. Jordan; Tianyi Lin; Emmanouil V. Vlatakis-Gkaragkounis (NeurIPS, 2022)

Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization. - Tianyi Lin; Zeyu Zheng; Michael I. Jordan (NeurIPS, 2022)

Perseus: A Simple High-Order Regularization Method for Variational Inequalities. - Tianyi Lin; Michael I. Jordan (CoRR, 2022)

A Continuous-Time Perspective on Monotone Equation Problems. - Tianyi Lin; Michael I. Jordan (CoRR, 2022)

Understanding Performance of Long-Document Ranking Models through Comprehensive Evaluation and Leaderboarding. - Leonid Boytsov; Tianyi Lin; Fangwei Gao; Yutian Zhao; Jeffrey Huang; Eric Nyberg (CoRR, 2022)

On the Complexity of Deterministic Nonsmooth and Nonconvex Optimization. - Michael I. Jordan; Tianyi Lin; Manolis Zampetakis (CoRR, 2022)

Explicit Second-Order Min-Max Optimization Methods with Optimal Convergence Guarantee. - Tianyi Lin; Panayotis Mertikopoulos; Michael I. Jordan (CoRR, 2022)

Accelerating Adaptive Cubic Regularization of Newton's Method via Random Sampling. - Xi Chen 010; Bo Jiang 007; Tianyi Lin; Shuzhong Zhang (J. Mach. Learn. Res., 2022)

On the Complexity of Approximating Multimarginal Optimal Transport. - Tianyi Lin; Nhat Ho; Marco Cuturi; Michael I. Jordan (J. Mach. Learn. Res., 2022)

On the Efficiency of Entropic Regularized Algorithms for Optimal Transport. - Tianyi Lin; Nhat Ho; Michael I. Jordan (J. Mach. Learn. Res., 2022)

A control-theoretic perspective on optimal high-order optimization. - Tianyi Lin; Michael I. Jordan (Math. Program., 2022)

Deterministic Nonsmooth Nonconvex Optimization. - Michael I. Jordan; Guy Kornowski; Tianyi Lin; Ohad Shamir; Manolis Zampetakis (COLT, 2023)

PPO-Based Energy-Efficient Power Control and Spectrum Allocation in In-Vehicle HetNets. - Tianyi Lin; Jun Du 001; Haijun Zhang 001; Arumugam Nallanathan; Jun Wang 012 (GLOBECOM, 2023)

Curvature-Independent Last-Iterate Convergence for Games on Riemannian Manifolds. - Yang Cai 001; Michael I. Jordan; Tianyi Lin; Argyris Oikonomou; Emmanouil V. Vlatakis-Gkaragkounis (CoRR, 2023)

First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems. - Michael I. Jordan; Tianyi Lin; Manolis Zampetakis (J. Mach. Learn. Res., 2023)

Monotone Inclusions, Acceleration, and Closed-Loop Control. - Tianyi Lin; Michael I. Jordan (Math. Oper. Res., 2023)

Structure-Driven Algorithm Design in Optimization and Machine Learning - Tianyi Lin (2023)

A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport. - Tianyi Lin; Marco Cuturi; Michael I. Jordan (AISTATS, 2024)

Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization. - Tianyi Lin; Chi Jin 001; Michael I. Jordan (CoRR, 2024)

Distributed Foundation Models for Multi-Modal Learning in 6G Wireless Networks. - Jun Du 001; Tianyi Lin; Chunxiao Jiang; Qianqian Yang; Faouzi Bader; Zhu Han 001 (IEEE Wirel. Commun., 2024)

Adaptive, Doubly Optimal No-Regret Learning in Strongly Monotone and Exp-Concave Games with Gradient Feedback. - Michael I. Jordan; Tianyi Lin; Zhengyuan Zhou (Oper. Res., 2025)

Perseus: a simple and optimal high-order method for variational inequalities. - Tianyi Lin; Michael I. Jordan (Math. Program., 2025)

ComPO: Preference Alignment via Comparison Oracles. - Peter Chen; Xi Chen; Wotao Yin; Tianyi Lin (CoRR, 2025)

Spectral Policy Optimization: Coloring your Incorrect Reasoning in GRPO. - Peter Chen; Xiaopeng Li; Ziniu Li; Xi Chen; Tianyi Lin (CoRR, 2025)

Intern-S1: A Scientific Multimodal Foundation Model. - Lei Bai 001; Zhongrui Cai; Yuhang Cao; Maosong Cao; Weihan Cao; Chiyu Chen; Haojiong Chen; Kai Chen; Pengcheng Chen; Ying Chen; Yongkang Chen; Yu Cheng; et al. (CoRR, 2025)