Tian Li - RankMe

48 papers A* 12A 1Misc 1Journal 30Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Asynchronous Heavy-Tailed Optimization. CoRR Junfei Sun, Dixi Yao, Xuchen Gong, Tahseen Rabbani, Manzil Zaheer, Tian Li
2025	J	`jnl`	Differentially Private Federated Clustering with Random Rebalancing. CoRR Xiyuan Yang, Shengyuan Hu, Soyeon Kim, Tian Li
2025	A*	`conf`	Efficient Distributed Optimization under Heavy-Tailed Noise. ICML Su Hyeong Lee, Manzil Zaheer, Tian Li
2025	J	`jnl`	Efficient Distributed Optimization under Heavy-Tailed Noise. CoRR Su Hyeong Lee, Manzil Zaheer, Tian Li
2025	A*	`conf`	Generalization and Robustness of the Tilted Empirical Risk. ICML Gholamali Aminian, Amir R. Asadi, Tian Li, Ahmad Beirami, Gesine Reinert, Samuel N. Cohen
2025	J	`jnl`	Hold Onto That Thought: Assessing KV Cache Compression On Reasoning. CoRR 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
2025	A*	`conf`	Many-Objective Multi-Solution Transport. ICLR Ziyue Li, Tian Li, Virginia Smith, Jeff A. Bilmes, Tianyi Zhou
2025	A*	`conf`	Tilted Sharpness-Aware Minimization. ICML Tian Li, Tianyi Zhou, Jeff A. Bilmes
2024	J	`jnl`	Efficient Adaptive Federated Optimization. CoRR Su Hyeong Lee, Sidharth Sharma, Manzil Zaheer, Tian Li
2024	J	`jnl`	Generalization Error of the Tilted Empirical Risk. CoRR Gholamali Aminian, Amir R. Asadi, Tian Li, Ahmad Beirami, Gesine Reinert, Samuel N. Cohen
2024	J	`jnl`	Many-Objective Multi-Solution Transport. CoRR Ziyue Li, Tian Li, Virginia Smith, Jeff A. Bilmes, Tianyi Zhou
2024	J	`jnl`	Maximizing Global Model Appeal in Federated Learning. Trans. Mach. Learn. Res. Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi
2024	J	`jnl`	Reweighting Local Mimina with Tilted SAM. CoRR Tian Li, Tianyi Zhou, Jeffrey A. Bilmes
2023	A*	`conf`	Differentially Private Adaptive Optimization with Delayed Preconditioners. ICLR Tian Li, Manzil Zaheer, Ken Liu, Sashank J. Reddi, Hugh Brendan McMahan, Virginia Smith
2023	J	`jnl`	On Tilted Losses in Machine Learning: Theory and Applications. J. Mach. Learn. Res. Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith
2022	J	`jnl`	Differentially Private Adaptive Optimization with Delayed Preconditioners. CoRR Tian Li, Manzil Zaheer, Ken Ziyu Liu, Sashank J. Reddi, H. Brendan McMahan, Virginia Smith
2022	A*	`conf`	Diverse Client Selection for Federated Learning via Submodular Maximization. ICLR Ravikumar Balakrishnan, Tian Li, Tianyi Zhou, Nageen Himayat, Virginia Smith, Jeff A. Bilmes
2022	J	`jnl`	Motley: Benchmarking Heterogeneity and Personalization in Federated Learning. CoRR Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao, Ken Ziyu Liu, Zheng Xu, Virginia Smith
2022	J	`jnl`	Private Adaptive Optimization with Side Information. CoRR Tian Li, Manzil Zaheer, Sashank J. Reddi, Virginia Smith
2022	A*	`conf`	Private Adaptive Optimization with Side information. ICML Tian Li, Manzil Zaheer, Sashank J. Reddi, Virginia Smith
2022	J	`jnl`	To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning. CoRR Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi
2021	J	`jnl`	A Field Guide to Federated Optimization. CoRR 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
2021	A*	`conf`	Ditto: Fair and Robust Federated Learning Through Personalization. ICML Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith
2021	A	`conf`	Ease.ML: A Lifecycle Management System for Machine Learning. CIDR 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
2021	A*	`conf`	Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. NeurIPS Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar
2021	J	`jnl`	Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. CoRR Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar
2021	A*	`conf`	Heterogeneity for the Win: One-Shot Federated Clustering. ICML Don Kurian Dennis, Tian Li, Virginia Smith
2021	J	`jnl`	Heterogeneity for the Win: One-Shot Federated Clustering. CoRR Don Kurian Dennis, Tian Li, Virginia Smith
2021	J	`jnl`	On Tilted Losses in Machine Learning: Theory and Applications. CoRR Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith
2021	A*	`conf`	Tilted Empirical Risk Minimization. ICLR Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith
2020	A*	`conf`	Fair Resource Allocation in Federated Learning. ICLR Tian Li, Maziar Sanjabi, Ahmad Beirami, Virginia Smith
2020	J	`jnl`	FedDANE: A Federated Newton-Type Method. CoRR Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
2020	J	`jnl`	Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Process. Mag. Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith
2020	J	`jnl`	Federated Multi-Task Learning for Competing Constraints. CoRR Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith
2020	—	`conf`	Federated Optimization in Heterogeneous Networks. MLSys Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
2020	—	`conf`	Learning Context-Aware Policies from Multiple Smart Homes via Federated Multi-Task Learning. IoTDI Tianlong Yu, Tian Li, Yuqiong Sun, Susanta Nanda, Virginia Smith, Vyas Sekar, Srinivasan Seshan
2020	J	`jnl`	Tilted Empirical Risk Minimization. CoRR Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith
2019	J	`jnl`	Enhancing the Privacy of Federated Learning with Sketching. CoRR Zaoxing Liu, Tian Li, Virginia Smith, Vyas Sekar
2019	J	`jnl`	Fair Resource Allocation in Federated Learning. CoRR Tian Li, Maziar Sanjabi, Virginia Smith
2019	Misc	`conf`	FedDANE: A Federated Newton-Type Method. ACSSC Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
2019	J	`jnl`	Federated Learning: Challenges, Methods, and Future Directions. CoRR Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith
2019	J	`jnl`	Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches. CoRR Tian Li, Zaoxing Liu, Vyas Sekar, Virginia Smith
2018	—	`conf`	CUTE: Querying Knowledge Graphs by Tabular Examples. APWeb/WAIM (1) Zichen Wang, Tian Li, Yingxia Shao, Bin Cui
2018	J	`jnl`	Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads. Proc. VLDB Endow. Tian Li, Jie Zhong, Ji Liu, Wentao Wu, Ce Zhang
2018	J	`jnl`	LEAF: A Benchmark for Federated Settings. CoRR Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar
2018	J	`jnl`	On the Convergence of Federated Optimization in Heterogeneous Networks. CoRR Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, Virginia Smith
2017	—	`conf`	An Overreaction to the Broken Machine Learning Abstraction: The ease.ml Vision. HILDA@SIGMOD Ce Zhang, Wentao Wu, Tian Li
2017	J	`jnl`	Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads. CoRR Tian Li, Jie Zhong, Ji Liu, Wentao Wu, Ce Zhang