| 2025 | IJCNLP | Positional Bias in Long-Document Ranking: Impact, Assessment, and Mitigation. | Leonid Boytsov, David Akinpelu, Nipun Katyal, Tianyi Lin, Fangwei Gao, Yutian Zhao, Jeffrey Huang, Eric Nyberg |
| 2024 | AISTATS | A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport. | Tianyi Lin, Marco Cuturi, Michael I. Jordan |
| 2023 | COLT | Deterministic Nonsmooth Nonconvex Optimization. | Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis |
| 2023 | GLOBECOM | PPO-Based Energy-Efficient Power Control and Spectrum Allocation in In-Vehicle HetNets. | Tianyi Lin, Jun Du, Haijun Zhang, Arumugam Nallanathan, Jun Wang |
| 2022 | AISTATS | On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms. | Nhat Ho, Tianyi Lin, Michael I. Jordan |
| 2022 | AISTATS | Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization. | Yaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael I. Jordan |
| 2022 | ICML | Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. | Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan |
| 2021 | AISTATS | On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification. | Tianyi Lin, Zeyu Zheng, Elynn Y. Chen, Marco Cuturi, Michael I. Jordan |
| 2021 | ICASSP | Relaxed Wasserstein with Applications to GANs. | Xin Guo, Johnny Hong, Tianyi Lin, Nan Yang |
| 2020 | COLT | Near-Optimal Algorithms for Minimax Optimization. | Tianyi Lin, Chi Jin, Michael I. Jordan |
| 2020 | ICML | On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. | Tianyi Lin, Chi Jin, Michael I. Jordan |
| 2020 | ICML | Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games. | Tianyi Lin, Zhengyuan Zhou, Panayotis Mertikopoulos, Michael I. Jordan |
| 2019 | ICML | On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms. | Tianyi Lin, Nhat Ho, Michael I. Jordan |
| 2019 | WSDM | Sparsemax and Relaxed Wasserstein for Topic Sparsity. | Tianyi Lin, Zhiyue Hu, Xin Guo |
| 2016 | CIKM | Understanding Sparse Topical Structure of Short Text via Stochastic Variational-Gibbs Inference. | Tianyi Lin, Siyuan Zhang, Hong Cheng |
| 2016 | ECAI | On Stochastic Primal-Dual Hybrid Gradient Approach for Compositely Regularized Minimization. | Linbo Qiao, Tianyi Lin, Yu-Gang Jiang, Fan Yang, Wei Liu, Xicheng Lu |
| 2014 | CIKM | Collaborative Filtering Incorporating Review Text and Co-clusters of Hidden User Communities and Item Groups. | Yinqing Xu, Wai Lam, Tianyi Lin |
| 2014 | CIKM | Latent Aspect Mining via Exploring Sparsity and Intrinsic Information. | Yinqing Xu, Tianyi Lin, Wai Lam, Zirui Zhou, Hong Cheng, Anthony Man-Cho So |
| 2014 | WWW | The dual-sparse topic model: mining focused topics and focused terms in short text. | Tianyi Lin, Wentao Tian, Qiaozhu Mei, Hong Cheng |