Tian Li

According to our database, Tian Li authored at least 29 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Maximizing Global Model Appeal in Federated Learning.

Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi

Trans. Mach. Learn. Res., 2024

Reweighting Local Mimina with Tilted SAM.

Tian Li, Tianyi Zhou, Jeffrey A. Bilmes

CoRR, 2024

Many-Objective Multi-Solution Transport.

Ziyue Li, Tian Li, Virginia Smith, Jeff Bilmes, Tianyi Zhou

CoRR, 2024

2023

On Tilted Losses in Machine Learning: Theory and Applications.

Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith

J. Mach. Learn. Res., 2023

Differentially Private Adaptive Optimization with Delayed Preconditioners.

Tian Li, Manzil Zaheer, Ken Liu, Sashank J. Reddi, Hugh Brendan McMahan, Virginia Smith

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Motley: Benchmarking Heterogeneity and Personalization in Federated Learning.

Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao, Ken Ziyu Liu, Zheng Xu, Virginia Smith

CoRR, 2022

To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning.

Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi

CoRR, 2022

Private Adaptive Optimization with Side information.

Tian Li, Manzil Zaheer, Sashank J. Reddi, Virginia Smith

Proceedings of the International Conference on Machine Learning, 2022

Diverse Client Selection for Federated Learning via Submodular Maximization.

Ravikumar Balakrishnan, Tian Li, Tianyi Zhou, Nageen Himayat, Virginia Smith, Jeff A. Bilmes

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

A Field Guide to Federated Optimization.

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

CoRR, 2021

Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing.

Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Heterogeneity for the Win: One-Shot Federated Clustering.

Don Kurian Dennis, Tian Li, Virginia Smith

Proceedings of the 38th International Conference on Machine Learning, 2021

Ditto: Fair and Robust Federated Learning Through Personalization.

Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith

Proceedings of the 38th International Conference on Machine Learning, 2021

Tilted Empirical Risk Minimization.

Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith

Proceedings of the 9th International Conference on Learning Representations, 2021

Ease.ML: A Lifecycle Management System for Machine Learning.

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

Proceedings of the 11th Conference on Innovative Data Systems Research, 2021

2020

Federated Learning: Challenges, Methods, and Future Directions.

Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith

IEEE Signal Process. Mag., 2020

Federated Multi-Task Learning for Competing Constraints.

Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith

CoRR, 2020

Federated Optimization in Heterogeneous Networks.

Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith

Proceedings of the Third Conference on Machine Learning and Systems, 2020

Learning Context-Aware Policies from Multiple Smart Homes via Federated Multi-Task Learning.

Tianlong Yu, Tian Li, Yuqiong Sun, Susanta Nanda, Virginia Smith, Vyas Sekar, Srinivasan Seshan

Proceedings of the Fifth IEEE/ACM International Conference on Internet-of-Things Design and Implementation, 2020

Fair Resource Allocation in Federated Learning.

Tian Li, Maziar Sanjabi, Ahmad Beirami, Virginia Smith

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Enhancing the Privacy of Federated Learning with Sketching.

Zaoxing Liu, Tian Li, Virginia Smith, Vyas Sekar

CoRR, 2019

Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches.

Tian Li, Zaoxing Liu, Vyas Sekar, Virginia Smith

CoRR, 2019

Fair Resource Allocation in Federated Learning.

Tian Li, Maziar Sanjabi, Virginia Smith

CoRR, 2019

FedDANE: A Federated Newton-Type Method.

Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith

Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

2018

Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads.

Tian Li, Jie Zhong, Ji Liu, Wentao Wu, Ce Zhang

Proc. VLDB Endow., 2018

On the Convergence of Federated Optimization in Heterogeneous Networks.

Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, Virginia Smith

CoRR, 2018

LEAF: A Benchmark for Federated Settings.

Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar

CoRR, 2018

CUTE: Querying Knowledge Graphs by Tabular Examples.

Zichen Wang, Tian Li, Yingxia Shao, Bin Cui

Proceedings of the Web and Big Data - Second International Joint Conference, 2018

2017

An Overreaction to the Broken Machine Learning Abstraction: The ease.ml Vision.

Ce Zhang, Wentao Wu, Tian Li

Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics, 2017