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Journal Articles
- 2024
[j1]Keith Rush, Zachary Charles, Zachary Garrett:
Federated Automatic Differentiation. J. Mach. Learn. Res. 25: 357:1-357:39 (2024)
Conference and Workshop Papers
- 2025
- [c8]Zachary Charles, Gabriel Teston, Lucio M. Dery, John Keith Rush, Nova Fallen, Zachary Garrett, Arthur D. Szlam, Arthur Douillard:
Communication-Efficient Language Model Training Scales Reliably and Robustly: Scaling Laws for DiLoCo. NeurIPS 2025 - [c7]Zachary Charles, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Nicole Mitchell, Krishna Pillutla, Keith Rush:
Learning with User-Level Differential Privacy Under Fixed Compute Budgets. SaTML 2025: 901-920 - 2023
- [c6]Christopher A. Choquette-Choo, Hugh Brendan McMahan, J. Keith Rush, Abhradeep Guha Thakurta:
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning. ICML 2023: 5924-5963 - [c5]Anastasia Koloskova, Ryan McKenna, Zachary Charles, John Keith Rush, H. Brendan McMahan:
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy. NeurIPS 2023 - 2022
- [c4]Zachary Charles, Keith Rush:
Iterated Vector Fields and Conservatism, with Applications to Federated Learning. ALT 2022: 130-147 - [c3]Gary Cheng, Zachary Charles, Zachary Garrett
, Keith Rush:
Does Federated Dropout actually work? CVPR Workshops 2022: 3386-3394 - 2021
- [c2]Peter Kairouz, Mónica Ribero Diaz, Keith Rush, Abhradeep Thakurta:
(Nearly) Dimension Independent Private ERM with AdaGrad Ratesvia Publicly Estimated Subspaces. COLT 2021: 2717-2746 - [c1]Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, Hugh Brendan McMahan:
Adaptive Federated Optimization. ICLR 2021
Informal and Other Publications
- 2026
- [i18]Keith Rush:
Global Convergence of Multiplicative Updates for the Matrix Mechanism: A Collaborative Proof with Gemini 3. CoRR abs/2603.19465 (2026) - [i17]Arthur Douillard, Keith Rush, Yani Donchev, Zachary Charles, Nova Fallen, Ayush Dubey, Ionel Gog, Josef Dean, Blake E. Woodworth, Zachary Garrett, Nate Keating, Jenny Bishop, Henry Prior, Edouard Yvinec, Arthur Szlam, Marc'Aurelio Ranzato, Jeff Dean:
Decoupled DiLoCo for Resilient Distributed Pre-training. CoRR abs/2604.21428 (2026) - 2025
- [i16]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:
Streaming DiLoCo with overlapping communication: Towards a Distributed Free Lunch. CoRR abs/2501.18512 (2025) - [i15]Zachary Charles, Gabriel Teston, Lucio M. Dery, Keith Rush, Nova Fallen, Zachary Garrett, Arthur Szlam, Arthur Douillard:
Communication-Efficient Language Model Training Scales Reliably and Robustly: Scaling Laws for DiLoCo. CoRR abs/2503.09799 (2025) - [i14]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:
Correlated Noise Mechanisms for Differentially Private Learning. CoRR abs/2506.08201 (2025) - 2024
- [i13]Keith Rush, Zachary Charles, Zachary Garrett:
FAX: Scalable and Differentiable Federated Primitives in JAX. CoRR abs/2403.07128 (2024) - [i12]Congchao Wang, Sean Augenstein, Keith Rush, Wittawat Jitkrittum, Harikrishna Narasimhan, Ankit Singh Rawat, Aditya Krishna Menon, Alec Go:
Cascade-Aware Training of Language Models. CoRR abs/2406.00060 (2024) - [i11]Zachary Charles, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Nicole Mitchell, Krishna Pillutla, Keith Rush:
Fine-Tuning Large Language Models with User-Level Differential Privacy. CoRR abs/2407.07737 (2024) - 2023
- [i10]Keith Rush, Zachary Charles, Zachary Garrett:
Federated Automatic Differentiation. CoRR abs/2301.07806 (2023) - [i9]Anastasia Koloskova, Ryan McKenna, Zachary Charles, Keith Rush, Brendan McMahan:
Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning. CoRR abs/2302.01463 (2023) - [i8]Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta, Zheng Xu:
(Amplified) Banded Matrix Factorization: A unified approach to private training. CoRR abs/2306.08153 (2023) - 2022
- [i7]Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta:
Private Online Prefix Sums via Optimal Matrix Factorizations. CoRR abs/2202.08312 (2022) - [i6]Christopher A. Choquette-Choo, H. Brendan McMahan, Keith Rush, Abhradeep Thakurta:
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning. CoRR abs/2211.06530 (2022) - 2021
- [i5]Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, Keith Rush, Sushant Prakash:
Federated Reconstruction: Partially Local Federated Learning. CoRR abs/2102.03448 (2021) - [i4]Zachary Charles, Keith Rush:
Iterated Vector Fields and Conservatism, with Applications to Federated Learning. CoRR abs/2109.03973 (2021) - 2020
- [i3]Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, H. Brendan McMahan:
Adaptive Federated Optimization. CoRR abs/2003.00295 (2020) - [i2]Peter Kairouz, Mónica Ribero, Keith Rush, Abhradeep Thakurta:
Dimension Independence in Unconstrained Private ERM via Adaptive Preconditioning. CoRR abs/2008.06570 (2020) - 2019
- [i1]Yihan Jiang, Jakub Konecný, Keith Rush, Sreeram Kannan:
Improving Federated Learning Personalization via Model Agnostic Meta Learning. CoRR abs/1909.12488 (2019)
Coauthor Index
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