Keith Rush

According to our database, Keith Rush authored at least 20 papers between 2019 and 2025.

Collaborative distances:

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

2025

Correlated Noise Mechanisms for Differentially Private Learning.

Krishna Pillutla, Jalaj Upadhyay, Christopher A. Choquette-Choo, Krishnamurthy Dvijotham, Arun Ganesh, Monika Henzinger, Jonathan Katz, Ryan McKenna, H. Brendan McMahan, Keith Rush, Thomas Steinke, Abhradeep Thakurta

CoRR, June, 2025

Communication-Efficient Language Model Training Scales Reliably and Robustly: Scaling Laws for DiLoCo.

Zachary Charles, Gabriel Teston, Lucio Dery, Keith Rush, Nova Fallen, Zachary Garrett, Arthur Szlam, Arthur Douillard

CoRR, March, 2025

Streaming DiLoCo with overlapping communication: Towards a Distributed Free Lunch.

Arthur Douillard, Yanislav Donchev, Keith Rush, Satyen Kale, Zachary Charles, Zachary Garrett, Gabriel Teston, Dave Lacey, Ross McIlroy, Jiajun Shen, Alexandre Ramé, Arthur Szlam, Marc'Aurelio Ranzato, Paul Barham

CoRR, January, 2025

Learning with User-Level Differential Privacy Under Fixed Compute Budgets.

Zachary Charles, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Nicole Mitchell, Krishna Pillutla, Keith Rush

Proceedings of the IEEE Conference on Secure and Trustworthy Machine Learning, 2025

2024

Fine-Tuning Large Language Models with User-Level Differential Privacy.

Zachary Charles, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Nicole Mitchell, Krishna Pillutla, Keith Rush

CoRR, 2024

Cascade-Aware Training of Language Models.

Congchao Wang, Sean Augenstein, Keith Rush, Wittawat Jitkrittum, Harikrishna Narasimhan, Ankit Singh Rawat, Aditya Krishna Menon, Alec Go

CoRR, 2024

FAX: Scalable and Differentiable Federated Primitives in JAX.

Keith Rush, Zachary Charles, Zachary Garrett

CoRR, 2024

2023

(Amplified) Banded Matrix Factorization: A unified approach to private training.

Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta, Zheng Xu

CoRR, 2023

Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning.

Anastasia Koloskova, Ryan McKenna, Zachary Charles, Keith Rush, Brendan McMahan

CoRR, 2023

Federated Automatic Differentiation.

Keith Rush, Zachary Charles, Zachary Garrett

CoRR, 2023

Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy.

Anastasia Koloskova, Ryan McKenna, Zachary Charles, John Keith Rush, H. Brendan McMahan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning.

Christopher A. Choquette-Choo, Hugh Brendan McMahan, J. Keith Rush, Abhradeep Guha Thakurta

Proceedings of the International Conference on Machine Learning, 2023

2022

Private Online Prefix Sums via Optimal Matrix Factorizations.

Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta

CoRR, 2022

Does Federated Dropout actually work?

Gary Cheng, Zachary Charles, Zachary Garrett, Keith Rush

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

Iterated Vector Fields and Conservatism, with Applications to Federated Learning.

Zachary Charles, Keith Rush

Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

2021

Federated Reconstruction: Partially Local Federated Learning.

Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, Keith Rush, Sushant Prakash

CoRR, 2021

Adaptive Federated Optimization.

Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, Hugh Brendan McMahan

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

(Nearly) Dimension Independent Private ERM with AdaGrad Ratesvia Publicly Estimated Subspaces.

Peter Kairouz, Mónica Ribero Diaz, Keith Rush, Abhradeep Thakurta

Proceedings of the Conference on Learning Theory, 2021

2020

Dimension Independence in Unconstrained Private ERM via Adaptive Preconditioning.

Peter Kairouz, Mónica Ribero, Keith Rush, Abhradeep Thakurta

CoRR, 2020

2019

Improving Federated Learning Personalization via Model Agnostic Meta Learning.

Yihan Jiang, Jakub Konecný, Keith Rush, Sreeram Kannan

CoRR, 2019