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2020 – today
- 2024
[j17]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): 20-30 (2024)- [c25]Tianyi Lin, Marco Cuturi, Michael I. Jordan:
A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport. AISTATS 2024: 145-153 - [i36]Tianyi Lin, Chi Jin, Michael I. Jordan:
Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization. CoRR abs/2408.11974 (2024) - 2023
- [b1]Tianyi Lin:
Structure-Driven Algorithm Design in Optimization and Machine Learning. University of California, Berkeley, USA, 2023 - [j16]Michael I. Jordan, Tianyi Lin, Manolis Zampetakis
:
First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems. J. Mach. Learn. Res. 24: 38:1-38:46 (2023) - [c24]Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis:
Deterministic Nonsmooth Nonconvex Optimization. COLT 2023: 4570-4597 - [c23]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: 6334-6339 - [i35]Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis:
Deterministic Nonsmooth Nonconvex Optimization. CoRR abs/2302.08300 (2023) - [i34]Yang Cai, Michael I. Jordan, Tianyi Lin, Argyris Oikonomou, Emmanouil V. Vlatakis-Gkaragkounis:
Curvature-Independent Last-Iterate Convergence for Games on Riemannian Manifolds. CoRR abs/2306.16617 (2023) - [i33]Michael I. Jordan, Tianyi Lin, Zhengyuan Zhou:
Adaptive, Doubly Optimal No-Regret Learning in Strongly Monotone and Exp-Concave Games with Gradient Feedback. CoRR abs/2310.14085 (2023) - [i32]Tianyi Lin, Marco Cuturi, Michael I. Jordan:
A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport. CoRR abs/2310.14087 (2023) - 2022
- [j15]Tianyi Lin, Nhat Ho, Marco Cuturi, Michael I. Jordan:
On the Complexity of Approximating Multimarginal Optimal Transport. J. Mach. Learn. Res. 23: 65:1-65:43 (2022) - [j14]Xi Chen, Bo Jiang, Tianyi Lin, Shuzhong Zhang:
Accelerating Adaptive Cubic Regularization of Newton's Method via Random Sampling. J. Mach. Learn. Res. 23: 90:1-90:38 (2022) - [j13]Tianyi Lin, Nhat Ho, Michael I. Jordan:
On the Efficiency of Entropic Regularized Algorithms for Optimal Transport. J. Mach. Learn. Res. 23: 137:1-137:42 (2022) - [j12]Tianyi Lin, Michael I. Jordan:
A control-theoretic perspective on optimal high-order optimization. Math. Program. 195(1): 929-975 (2022) - [c22]Nhat Ho, Tianyi Lin, Michael I. Jordan:
On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms. AISTATS 2022: 896-921 - [c21]Yaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael I. Jordan:
Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization. AISTATS 2022: 1219-1250 - [c20]Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan:
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. ICML 2022: 13441-13467 - [c19]Michael I. Jordan, Tianyi Lin, Emmanouil V. Vlatakis-Gkaragkounis:
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces. NeurIPS 2022 - [c18]Tianyi Lin, Zeyu Zheng, Michael I. Jordan:
Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization. NeurIPS 2022 - [i31]Michael I. Jordan, Tianyi Lin, Manolis Zampetakis:
First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems. CoRR abs/2204.03132 (2022) - [i30]Tianyi Lin, Michael I. Jordan:
Perseus: A Simple High-Order Regularization Method for Variational Inequalities. CoRR abs/2205.03202 (2022) - [i29]Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan:
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. CoRR abs/2205.07217 (2022) - [i28]Michael I. Jordan, Tianyi Lin, Emmanouil V. Vlatakis-Gkaragkounis:
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces. CoRR abs/2206.02041 (2022) - [i27]Tianyi Lin, Michael I. Jordan:
A Continuous-Time Perspective on Monotone Equation Problems. CoRR abs/2206.04770 (2022) - [i26]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 abs/2207.01262 (2022) - [i25]Tianyi Lin, Zeyu Zheng, Michael I. Jordan:
Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization. CoRR abs/2209.05045 (2022) - [i24]Michael I. Jordan, Tianyi Lin, Manolis Zampetakis:
On the Complexity of Deterministic Nonsmooth and Nonconvex Optimization. CoRR abs/2209.12463 (2022) - [i23]Tianyi Lin, Panayotis Mertikopoulos, Michael I. Jordan:
Explicit Second-Order Min-Max Optimization Methods with Optimal Convergence Guarantee. CoRR abs/2210.12860 (2022) - 2021
- [j11]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): 389-424 (2021) - [c17]Tianyi Lin, Zeyu Zheng, Elynn Y. Chen, Marco Cuturi, Michael I. Jordan:
On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification. AISTATS 2021: 262-270 - [c16]Wenshuo Guo, Michael I. Jordan, Tianyi Lin:
A Variational Inequality Approach to Bayesian Regression Games. CDC 2021: 795-802 - [c15]Xin Guo, Johnny Hong, Tianyi Lin, Nan Yang:
Relaxed Wasserstein with Applications to GANs. ICASSP 2021: 3325-3329 - [i22]Wenshuo Guo, Michael I. Jordan, Tianyi Lin:
A Variational Inequality Approach to Bayesian Regression Games. CoRR abs/2103.13509 (2021) - [i21]Yaodong Yu, Tianyi Lin, Eric Mazumdar, Michael I. Jordan:
Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization. CoRR abs/2104.13326 (2021) - [i20]Tianyi Lin, Michael I. Jordan:
On Monotone Inclusions, Acceleration and Closed-Loop Control. CoRR abs/2111.08093 (2021) - [i19]Tianyi Lin, Zhengyuan Zhou, Wenjia Ba, Jiawei Zhang:
Optimal No-Regret Learning in Strongly Monotone Games with Bandit Feedback. CoRR abs/2112.02856 (2021) - 2020
- [j10]Bo Jiang, Tianyi Lin, Shuzhong Zhang:
A Unified Adaptive Tensor Approximation Scheme to Accelerate Composite Convex Optimization. SIAM J. Optim. 30(4): 2897-2926 (2020) - [c14]Tianyi Lin, Chengyou Fan, Mengdi Wang, Michael I. Jordan:
Improved Sample Complexity for Stochastic Compositional Variance Reduced Gradient. ACC 2020: 126-131 - [c13]Ilan Adler, Zhiyue Tom Hu, Tianyi Lin:
New Proximal Newton-Type Methods for Convex Optimization. CDC 2020: 4828-4835 - [c12]Tianyi Lin, Chi Jin, Michael I. Jordan:
Near-Optimal Algorithms for Minimax Optimization. COLT 2020: 2738-2779 - [c11]Tianyi Lin, Chi Jin, Michael I. Jordan:
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. ICML 2020: 6083-6093 - [c10]Tianyi Lin, Zhengyuan Zhou, Panayotis Mertikopoulos, Michael I. Jordan:
Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games. ICML 2020: 6161-6171 - [c9]Tianyi Lin, Chenyou Fan, Nhat Ho, Marco Cuturi, Michael I. Jordan:
Projection Robust Wasserstein Distance and Riemannian Optimization. NeurIPS 2020 - [c8]Tianyi Lin, Nhat Ho, Xi Chen, Marco Cuturi, Michael I. Jordan:
Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm. NeurIPS 2020 - [i18]Tianyi Lin, Chi Jin, Michael I. Jordan:
Near-Optimal Algorithms for Minimax Optimization. CoRR abs/2002.02417 (2020) - [i17]Tianyi Lin, Nhat Ho, Xi Chen, Marco Cuturi, Michael I. Jordan:
Revisiting Fixed Support Wasserstein Barycenter: Computational Hardness and Efficient Algorithms. CoRR abs/2002.04783 (2020) - [i16]Tianyi Lin, Zhengyuan Zhou, Panayotis Mertikopoulos, Michael I. Jordan:
Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games. CoRR abs/2002.09806 (2020) - [i15]Tianyi Lin, Chenyou Fan, Nhat Ho, Marco Cuturi, Michael I. Jordan:
Projection Robust Wasserstein Distance and Riemannian Optimization. CoRR abs/2006.07458 (2020) - [i14]Tianyi Lin, Zeyu Zheng, Elynn Y. Chen, Marco Cuturi, Michael I. Jordan:
On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification. CoRR abs/2006.12301 (2020)
2010 – 2019
- 2019
- [j9]Bo Jiang, Tianyi Lin, Shiqian Ma, Shuzhong Zhang:
Structured nonconvex and nonsmooth optimization: algorithms and iteration complexity analysis. Comput. Optim. Appl. 72(1): 115-157 (2019) - [c7]Tianyi Lin, Nhat Ho, Michael I. Jordan:
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms. ICML 2019: 3982-3991 - [c6]Tianyi Lin, Zhiyue Hu, Xin Guo:
Sparsemax and Relaxed Wasserstein for Topic Sparsity. WSDM 2019: 141-149 - [i13]Tianyi Lin, Nhat Ho, Michael I. Jordan:
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms. CoRR abs/1901.06482 (2019) - [i12]Nhat Ho, Tianyi Lin, Michael I. Jordan:
Global Error Bounds and Linear Convergence for Gradient-Based Algorithms for Trend Filtering and 𝓁1-Convex Clustering. CoRR abs/1904.07462 (2019) - [i11]Tianyi Lin, Chi Jin, Michael I. Jordan:
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. CoRR abs/1906.00331 (2019) - [i10]Tianyi Lin, Nhat Ho, Michael I. Jordan:
On the Acceleration of the Sinkhorn and Greenkhorn Algorithms for Optimal Transport. CoRR abs/1906.01437 (2019) - [i9]Tianyi Lin, Nhat Ho, Marco Cuturi, Michael I. Jordan:
On the Complexity of Approximating Multimarginal Optimal Transport. CoRR abs/1910.00152 (2019) - [i8]Tianyi Lin, Michael I. Jordan:
A Control-Theoretic Perspective on Optimal High-Order Optimization. CoRR abs/1912.07168 (2019) - 2018
- [j8]Tianyi Lin, Linbo Qiao, Teng Zhang, Jiashi Feng, Bofeng Zhang:
Stochastic Primal-Dual Proximal ExtraGradient descent for compositely regularized optimization. Neurocomputing 273: 516-525 (2018) - [j7]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: 78-90 (2018) - [j6]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): 69-88 (2018) - [j5]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): 5-20 (2018) - [i7]Linbo Qiao, Tianyi Lin, Qi Qin, Xicheng Lu:
On the Iteration Complexity Analysis of Stochastic Primal-Dual Hybrid Gradient Approach with High Probability. CoRR abs/1801.06934 (2018) - [i6]Tianyi Lin, Chenyou Fan, Mengdi Wang, Michael I. Jordan:
Improved Oracle Complexity for Stochastic Compositional Variance Reduced Gradient. CoRR abs/1806.00458 (2018) - [i5]Tianyi Lin, Zhiyue Hu, Xin Guo:
Sparsemax and Relaxed Wasserstein for Topic Sparsity. CoRR abs/1810.09079 (2018) - 2017
- [j4]Tianyi Lin, Shiqian Ma, Shuzhong Zhang:
An Extragradient-Based Alternating Direction Method for Convex Minimization. Found. Comput. Math. 17(1): 35-59 (2017) - [j3]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): 221-254 (2017) - [i4]Xin Guo, Johnny Hong, Tianyi Lin, Nan Yang:
Relaxed Wasserstein with Applications to GANs. CoRR abs/1705.07164 (2017) - [i3]Tianyi Lin, Linbo Qiao, Teng Zhang, Jiashi Feng, Bofeng Zhang:
Stochastic Primal-Dual Proximal ExtraGradient descent for compositely regularized optimization. CoRR abs/1708.05978 (2017) - 2016
- [j2]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): 52-81 (2016) - [c5]Tianyi Lin, Siyuan Zhang, Hong Cheng:
Understanding Sparse Topical Structure of Short Text via Stochastic Variational-Gibbs Inference. CIKM 2016: 407-416 - [c4]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: 167-174 - [i2]Bo Jiang, Tianyi Lin, Shiqian Ma, Shuzhong Zhang:
Structured Nonconvex and Nonsmooth Optimization: Algorithms and Iteration Complexity Analysis. CoRR abs/1605.02408 (2016) - 2015
- [j1]Tianyi Lin, Shiqian Ma, Shuzhong Zhang:
On the Global Linear Convergence of the ADMM with MultiBlock Variables. SIAM J. Optim. 25(3): 1478-1497 (2015) - [i1]Tianyi Lin, Shiqian Ma, Shuzhong Zhang:
Global Convergence of Unmodified 3-Block ADMM for a Class of Convex Minimization Problems. CoRR abs/1505.04252 (2015) - 2014
- [c3]Yinqing Xu, Wai Lam, Tianyi Lin:
Collaborative Filtering Incorporating Review Text and Co-clusters of Hidden User Communities and Item Groups. CIKM 2014: 251-260 - [c2]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: 879-888 - [c1]Tianyi Lin, Wentao Tian, Qiaozhu Mei, Hong Cheng:
The dual-sparse topic model: mining focused topics and focused terms in short text. WWW 2014: 539-550
Coauthor Index
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