Tian Li

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 37

Top Topics

Future Directions

Machine Learning

Resource Allocation

Top Venues

Proc. VLDB Endow.

Publications

Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi
Maximizing Global Model Appeal in Federated Learning. Trans. Mach. Learn. Res. 2024 (2024)
Ziyue Li, Tian Li, Virginia Smith, Jeff Bilmes, Tianyi Zhou
Many-Objective Multi-Solution Transport. CoRR (2024)
Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith
On Tilted Losses in Machine Learning: Theory and Applications. J. Mach. Learn. Res. 24 (2023)
Tian Li, Manzil Zaheer, Ken Liu, Sashank J. Reddi, Hugh Brendan McMahan, Virginia Smith
Differentially Private Adaptive Optimization with Delayed Preconditioners. ICLR (2023)
Tian Li, Manzil Zaheer, Sashank J. Reddi, Virginia Smith
Private Adaptive Optimization with Side Information. CoRR (2022)
Ravikumar Balakrishnan, Tian Li, Tianyi Zhou, Nageen Himayat, Virginia Smith, Jeff A. Bilmes
Diverse Client Selection for Federated Learning via Submodular Maximization. ICLR (2022)
Tian Li, Manzil Zaheer, Ken Ziyu Liu, Sashank J. Reddi, H. Brendan McMahan, Virginia Smith
Differentially Private Adaptive Optimization with Delayed Preconditioners. CoRR (2022)
Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi
To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning. CoRR (2022)
Tian Li, Manzil Zaheer, Sashank J. Reddi, Virginia Smith
Private Adaptive Optimization with Side information. ICML (2022)
Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao, Ziyu Liu, Zheng Xu, Virginia Smith
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning. CoRR (2022)
Don Kurian Dennis, Tian Li, Virginia Smith
Heterogeneity for the Win: One-Shot Federated Clustering. ICML (2021)
Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith
Tilted Empirical Risk Minimization. ICLR (2021)
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 Horvath, 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 (2021)
Don Kurian Dennis, Tian Li, Virginia Smith
Heterogeneity for the Win: One-Shot Federated Clustering. CoRR (2021)
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)
Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith
Ditto: Fair and Robust Federated Learning Through Personalization. ICML (2021)
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 (2021)
Leonel Aguilar Melgar, David Dao, Shaoduo Gan, Nezihe Merve Gürel, Nora Hollenstein, Jiawei Jiang, Bojan Karlas, Thomas Lemmin, Tian Li, Yang Li, 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)
Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith
On Tilted Losses in Machine Learning: Theory and Applications. CoRR (2021)
Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith
Tilted Empirical Risk Minimization. CoRR (2020)
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
FedDANE: A Federated Newton-Type Method. CoRR (2020)
Tian Li, Maziar Sanjabi, Ahmad Beirami, Virginia Smith
Fair Resource Allocation in Federated Learning. ICLR (2020)
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)
Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith
Federated Multi-Task Learning for Competing Constraints. CoRR (2020)
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith
Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Process. Mag. 37 (3) (2020)
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
Federated Optimization in Heterogeneous Networks. MLSys (2020)
Zaoxing Liu, Tian Li, Virginia Smith, Vyas Sekar
Enhancing the Privacy of Federated Learning with Sketching. CoRR (2019)
Tian Li, Zaoxing Liu, Vyas Sekar, Virginia Smith
Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches. CoRR (2019)
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
FedDANE: A Federated Newton-Type Method. ACSSC (2019)
Tian Li, Maziar Sanjabi, Virginia Smith
Fair Resource Allocation in Federated Learning. CoRR (2019)
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith
Federated Learning: Challenges, Methods, and Future Directions. CoRR (2019)
Zichen Wang, Tian Li, Yingxia Shao, Bin Cui
CUTE: Querying Knowledge Graphs by Tabular Examples. APWeb/WAIM (1) (2018)
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) (2018)
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar
LEAF: A Benchmark for Federated Settings. CoRR (2018)
Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, Virginia Smith
On the Convergence of Federated Optimization in Heterogeneous Networks. CoRR (2018)
Ce Zhang, Wentao Wu, Tian Li
An Overreaction to the Broken Machine Learning Abstraction: The ease.ml Vision. HILDA@SIGMOD (2017)
Tian Li, Jie Zhong, Ji Liu, Wentao Wu, Ce Zhang
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads. CoRR (2017)