An Overreaction to the Broken Machine Learning Abstraction: The ease.ml Vision. - Ce Zhang 001; Wentao Wu 001; Tian Li 005 (HILDA@SIGMOD, 2017)

CUTE: Querying Knowledge Graphs by Tabular Examples. - Zichen Wang; Tian Li 005; Yingxia Shao; Bin Cui 001 (APWeb/WAIM (1), 2018)

LEAF: A Benchmark for Federated Settings. - Sebastian Caldas; Peter Wu; Tian Li 005; Jakub Konecný; H. Brendan McMahan; Virginia Smith; Ameet Talwalkar (CoRR, 2018)

On the Convergence of Federated Optimization in Heterogeneous Networks. - Anit Kumar Sahu; Tian Li 005; Maziar Sanjabi; Manzil Zaheer; Ameet Talwalkar; Virginia Smith (CoRR, 2018)

Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads. - Tian Li 005; Jie Zhong; Ji Liu 002; Wentao Wu 001; Ce Zhang 001 (Proc. VLDB Endow., 2018)

FedDANE: A Federated Newton-Type Method. - Tian Li 005; Anit Kumar Sahu; Manzil Zaheer; Maziar Sanjabi; Ameet Talwalkar; Virginia Smith (ACSSC, 2019)

Fair Resource Allocation in Federated Learning. - Tian Li 005; Maziar Sanjabi; Virginia Smith (CoRR, 2019)

Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches. - Tian Li 005; Zaoxing Liu; Vyas Sekar; Virginia Smith (CoRR, 2019)

Enhancing the Privacy of Federated Learning with Sketching. - Zaoxing Liu; Tian Li 005; Virginia Smith; Vyas Sekar (CoRR, 2019)

Fair Resource Allocation in Federated Learning. - Tian Li 005; Maziar Sanjabi; Ahmad Beirami; Virginia Smith (ICLR, 2020)

Learning Context-Aware Policies from Multiple Smart Homes via Federated Multi-Task Learning. - Tianlong Yu; Tian Li 005; Yuqiong Sun; Susanta Nanda; Virginia Smith; Vyas Sekar; Srinivasan Seshan (IoTDI, 2020)

Federated Optimization in Heterogeneous Networks. - Tian Li 005; Anit Kumar Sahu; Manzil Zaheer; Maziar Sanjabi; Ameet Talwalkar; Virginia Smith (MLSys, 2020)

Federated Multi-Task Learning for Competing Constraints. - Tian Li 005; Shengyuan Hu 001; Ahmad Beirami; Virginia Smith (CoRR, 2020)

Federated Learning: Challenges, Methods, and Future Directions. - Tian Li 005; Anit Kumar Sahu; Ameet Talwalkar; Virginia Smith (IEEE Signal Process. Mag., 2020)

Ease.ML: A Lifecycle Management System for Machine Learning. - Leonel Aguilar Melgar; David Dao; Shaoduo Gan; Nezihe Merve Gürel; Nora Hollenstein; Jiawei Jiang 001; Bojan Karlas; Thomas Lemmin; Tian Li 005; Yang Li 106; Susie Xi Rao; Johannes Rausch; et al. (CIDR, 2021)

Tilted Empirical Risk Minimization. - Tian Li 005; Ahmad Beirami; Maziar Sanjabi; Virginia Smith (ICLR, 2021)

Ditto: Fair and Robust Federated Learning Through Personalization. - Tian Li 005; Shengyuan Hu 001; Ahmad Beirami; Virginia Smith (ICML, 2021)

Heterogeneity for the Win: One-Shot Federated Clustering. - Don Kurian Dennis; Tian Li 005; Virginia Smith (ICML, 2021)

Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. - Mikhail Khodak; Renbo Tu; Tian Li 005; Liam Li; Maria-Florina Balcan; Virginia Smith; Ameet Talwalkar (NeurIPS, 2021)

A Field Guide to Federated Optimization. - Jianyu Wang; Zachary Charles; Zheng Xu 002; Gauri Joshi; H. Brendan McMahan; Blaise Agüera y Arcas; Maruan Al-Shedivat; Galen Andrew; Salman Avestimehr; Katharine Daly; Deepesh Data; Suhas N. Diggavi; et al. (CoRR, 2021)

Diverse Client Selection for Federated Learning via Submodular Maximization. - Ravikumar Balakrishnan; Tian Li 005; Tianyi Zhou 001; Nageen Himayat; Virginia Smith; Jeff A. Bilmes (ICLR, 2022)

Private Adaptive Optimization with Side information. - Tian Li 005; Manzil Zaheer; Sashank J. Reddi; Virginia Smith (ICML, 2022)

To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning. - Yae Jee Cho; Divyansh Jhunjhunwala; Tian Li 005; Virginia Smith; Gauri Joshi (CoRR, 2022)

Motley: Benchmarking Heterogeneity and Personalization in Federated Learning. - Shanshan Wu; Tian Li 005; Zachary Charles; Yu Xiao; Ken Ziyu Liu; Zheng Xu 002; Virginia Smith (CoRR, 2022)

Differentially Private Adaptive Optimization with Delayed Preconditioners. - Tian Li 005; Manzil Zaheer; Ken Liu; Sashank J. Reddi; Hugh Brendan McMahan; Virginia Smith (ICLR, 2023)

On Tilted Losses in Machine Learning: Theory and Applications. - Tian Li 005; Ahmad Beirami; Maziar Sanjabi; Virginia Smith (J. Mach. Learn. Res., 2023)

Reweighting Local Mimina with Tilted SAM. - Tian Li 005; Tianyi Zhou 001; Jeffrey A. Bilmes (CoRR, 2024)

Maximizing Global Model Appeal in Federated Learning. - Yae Jee Cho; Divyansh Jhunjhunwala; Tian Li 005; Virginia Smith; Gauri Joshi (Trans. Mach. Learn. Res., 2024)

Many-Objective Multi-Solution Transport. - Ziyue Li; Tian Li 005; Virginia Smith; Jeff Bilmes; Tianyi Zhou 001 (ICLR, 2025)

Differentially Private Federated Clustering with Random Rebalancing. - Xiyuan Yang; Shengyuan Hu 001; Soyeon Kim; Tian Li 005 (CoRR, 2025)