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ORCIDTian Li 0005
Person information
- affiliation: University of Chicago, IL, USA
- affiliation (PhD 2023): Carnegie Mellon University, Pittsburgh, PA, USA
Other persons with the same name
- Tian Li — disambiguation page
- Tian Li 0001
![[0000-0003-3985-0929]](https://dblp.org/img/orcid-mark.12x12.png "0000-0003-3985-0929")
— 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang, China (and 2 more) - Tian Li 0002 — Southwest University, College of Computer and Information Science, Chongqing, China
- Tian Li 0003 — Peking University, Beijing, China
- Tian Li 0004 — Beijing Normal University, College of Global Change and Earth System Science (GCESS), State Key Laboratory of Remote Sensing Science, China
- Tian Li 0006 — University of Melbourne, Melbourne, Australia
- Tian Li 0007 — RWTH Aachen University, Aachen, Germany
- Tian Li 0008 — Nanjing Vocational University of Industry Technology, Nanjing, China
- Tian Li 0009 — Beihang University, Hangzhou, China
- Tian Li 0010 — Liaoning Normal University, Dalian, China
- Tian Li 0011 — Southwest University, Beibei, Chongqing, China
- Tian Li 0012 — Hong Kong Polytechnic University, Kowloon, Hong Kong, SAR, China
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2020 – today
- 2025
[c18]Ziyue Li, Tian Li, Virginia Smith, Jeff A. Bilmes, Tianyi Zhou:
Many-Objective Multi-Solution Transport. ICLR 2025- [c17]Tian Li, Tianyi Zhou, Jeff A. Bilmes:
Tilted Sharpness-Aware Minimization. ICML 2025 - [c16]Gholamali Aminian, Amir R. Asadi, Tian Li, Ahmad Beirami, Gesine Reinert, Samuel N. Cohen:
Generalization and Robustness of the Tilted Empirical Risk. ICML 2025 - [c15]Su Hyeong Lee, Manzil Zaheer, Tian Li:
Efficient Distributed Optimization under Heavy-Tailed Noise. ICML 2025 - [i25]Su Hyeong Lee, Manzil Zaheer, Tian Li:
Efficient Distributed Optimization under Heavy-Tailed Noise. CoRR abs/2502.04164 (2025) - [i24]Xiyuan Yang, Shengyuan Hu, Soyeon Kim, Tian Li:
Differentially Private Federated Clustering with Random Rebalancing. CoRR abs/2508.06183 (2025) - [i23]Minghui Liu, Aadi Palnitkar, Tahseen Rabbani, Hyunwoo Jae, Kyle Rui Sang, Dixi Yao, Shayan Shabihi, Fuheng Zhao, Tian Li, Ce Zhang, Furong Huang, Kunpeng Zhang:
Hold Onto That Thought: Assessing KV Cache Compression On Reasoning. CoRR abs/2512.12008 (2025) - 2024
- [j4]Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi:
Maximizing Global Model Appeal in Federated Learning. Trans. Mach. Learn. Res. 2024 (2024) - [i22]Ziyue Li, Tian Li, Virginia Smith, Jeff A. Bilmes, Tianyi Zhou:
Many-Objective Multi-Solution Transport. CoRR abs/2403.04099 (2024) - [i21]Gholamali Aminian, Amir R. Asadi, Tian Li, Ahmad Beirami, Gesine Reinert, Samuel N. Cohen:
Generalization Error of the Tilted Empirical Risk. CoRR abs/2409.19431 (2024) - [i20]Su Hyeong Lee, Sidharth Sharma, Manzil Zaheer, Tian Li:
Efficient Adaptive Federated Optimization. CoRR abs/2410.18117 (2024) - [i19]Tian Li, Tianyi Zhou, Jeffrey A. Bilmes:
Reweighting Local Mimina with Tilted SAM. CoRR abs/2410.22656 (2024) - 2023
- [j3]Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith:
On Tilted Losses in Machine Learning: Theory and Applications. J. Mach. Learn. Res. 24: 142:1-142:79 (2023) - [c14]Tian Li, Manzil Zaheer, Ken Liu, Sashank J. Reddi, Hugh Brendan McMahan, Virginia Smith:
Differentially Private Adaptive Optimization with Delayed Preconditioners. ICLR 2023 - 2022
- [c13]Ravikumar Balakrishnan, Tian Li, Tianyi Zhou, Nageen Himayat, Virginia Smith, Jeff A. Bilmes:
Diverse Client Selection for Federated Learning via Submodular Maximization. ICLR 2022 - [c12]Tian Li, Manzil Zaheer, Sashank J. Reddi, Virginia Smith:
Private Adaptive Optimization with Side information. ICML 2022: 13086-13105 - [i18]Tian Li, Manzil Zaheer, Sashank J. Reddi, Virginia Smith:
Private Adaptive Optimization with Side Information. CoRR abs/2202.05963 (2022) - [i17]Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi:
To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning. CoRR abs/2205.14840 (2022) - [i16]Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao, Ken Ziyu Liu, Zheng Xu, Virginia Smith:
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning. CoRR abs/2206.09262 (2022) - [i15]Tian Li, Manzil Zaheer, Ken Ziyu Liu, Sashank J. Reddi, H. Brendan McMahan, Virginia Smith:
Differentially Private Adaptive Optimization with Delayed Preconditioners. CoRR abs/2212.00309 (2022) - 2021
- [c11]Leonel Aguilar Melgar, David Dao, Shaoduo Gan, Nezihe Merve Gürel, Nora Hollenstein, Jiawei Jiang, Bojan Karlas, Thomas Lemmin, Tian Li, Yang Li, Susie Xi Rao, Johannes Rausch, Cédric Renggli, Luka Rimanic, Maurice Weber, Shuai Zhang, Zhikuan Zhao, Kevin Schawinski, Wentao Wu, Ce Zhang:
Ease.ML: A Lifecycle Management System for Machine Learning. CIDR 2021 - [c10]Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith:
Tilted Empirical Risk Minimization. ICLR 2021 - [c9]Don Kurian Dennis, Tian Li, Virginia Smith:
Heterogeneity for the Win: One-Shot Federated Clustering. ICML 2021: 2611-2620 - [c8]Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith:
Ditto: Fair and Robust Federated Learning Through Personalization. ICML 2021: 6357-6368 - [c7]Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar:
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. NeurIPS 2021: 19184-19197 - [i14]Don Kurian Dennis, Tian Li, Virginia Smith:
Heterogeneity for the Win: One-Shot Federated Clustering. CoRR abs/2103.00697 (2021) - [i13]Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar:
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. CoRR abs/2106.04502 (2021) - [i12]Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horváth, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu:
A Field Guide to Federated Optimization. CoRR abs/2107.06917 (2021) - [i11]Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith:
On Tilted Losses in Machine Learning: Theory and Applications. CoRR abs/2109.06141 (2021) - 2020
- [j2]Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith:
Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Process. Mag. 37(3): 50-60 (2020) - [c6]Tian Li, Maziar Sanjabi, Ahmad Beirami, Virginia Smith:
Fair Resource Allocation in Federated Learning. ICLR 2020 - [c5]Tianlong Yu, Tian Li, Yuqiong Sun, Susanta Nanda, Virginia Smith, Vyas Sekar, Srinivasan Seshan:
Learning Context-Aware Policies from Multiple Smart Homes via Federated Multi-Task Learning. IoTDI 2020: 104-115 - [c4]Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith:
Federated Optimization in Heterogeneous Networks. MLSys 2020 - [i10]Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith:
FedDANE: A Federated Newton-Type Method. CoRR abs/2001.01920 (2020) - [i9]Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith:
Tilted Empirical Risk Minimization. CoRR abs/2007.01162 (2020) - [i8]Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith:
Federated Multi-Task Learning for Competing Constraints. CoRR abs/2012.04221 (2020)
2010 – 2019
- 2019
- [c3]Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith:
FedDANE: A Federated Newton-Type Method. ACSSC 2019: 1227-1231 - [i7]Tian Li, Maziar Sanjabi, Virginia Smith:
Fair Resource Allocation in Federated Learning. CoRR abs/1905.10497 (2019) - [i6]Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith:
Federated Learning: Challenges, Methods, and Future Directions. CoRR abs/1908.07873 (2019) - [i5]Tian Li, Zaoxing Liu, Vyas Sekar, Virginia Smith:
Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches. CoRR abs/1911.00972 (2019) - [i4]Zaoxing Liu, Tian Li, Virginia Smith, Vyas Sekar:
Enhancing the Privacy of Federated Learning with Sketching. CoRR abs/1911.01812 (2019) - 2018
- [j1]Tian Li, Jie Zhong, Ji Liu, Wentao Wu, Ce Zhang:
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads. Proc. VLDB Endow. 11(5): 607-620 (2018) - [c2]Zichen Wang, Tian Li, Yingxia Shao, Bin Cui:
CUTE: Querying Knowledge Graphs by Tabular Examples. APWeb/WAIM (1) 2018: 461-465 - [i3]Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar:
LEAF: A Benchmark for Federated Settings. CoRR abs/1812.01097 (2018) - [i2]Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, Virginia Smith:
On the Convergence of Federated Optimization in Heterogeneous Networks. CoRR abs/1812.06127 (2018) - 2017
- [c1]Ce Zhang, Wentao Wu, Tian Li:
An Overreaction to the Broken Machine Learning Abstraction: The ease.ml Vision. HILDA@SIGMOD 2017: 3:1-3:6 - [i1]Tian Li, Jie Zhong, Ji Liu, Wentao Wu, Ce Zhang:
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads. CoRR abs/1708.07308 (2017)
Coauthor Index
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