Minibatch Stochastic Approximate Proximal Point Methods. - Hilal Asi; Karan N. Chadha; Gary Cheng 004; John C. Duchi (NeurIPS, 2020)

Fine-tuning is Fine in Federated Learning. - Gary Cheng 004; Karan N. Chadha; John C. Duchi (CoRR, 2021)

Approximate Function Evaluation via Multi-Armed Bandits. - Tavor Z. Baharav; Gary Cheng 004; Mert Pilanci; David Tse (AISTATS, 2022)

Does Federated Dropout actually work? - Gary Cheng 004; Zachary Charles; Zachary Garrett; Keith Rush (CVPR Workshops, 2022)

Private optimization in the interpolation regime: faster rates and hardness results. - Hilal Asi; Karan N. Chadha; Gary Cheng 004; John C. Duchi (ICML, 2022)

Accelerated, Optimal and Parallel: Some results on model-based stochastic optimization. - Karan N. Chadha; Gary Cheng 004; John C. Duchi (ICML, 2022)

Federated Asymptotics: a model to compare federated learning algorithms. - Gary Cheng 004; Karan N. Chadha; John C. Duchi (AISTATS, 2023)

Collaboratively Learning Linear Models with Structured Missing Data. - Chen Cheng; Gary Cheng 004; John C. Duchi (NeurIPS, 2023)

Causal Inference out of Control: Estimating the Steerability of Consumption. - Gary Cheng 004; Moritz Hardt; Celestine Mendler-Dünner (CoRR, 2023)

Causal Inference out of Control: Estimating Performativity without Treatment Randomization. - Gary Cheng 004; Moritz Hardt; Celestine Mendler-Dünner (ICML, 2024)