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2020 – today
- 2025
[i14]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)- 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
- [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 - [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
- [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 - [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
- [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 - [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) - no results
2010 – 2019
- 2019
- [i1]Yihan Jiang, Jakub Konecný, Keith Rush, Sreeram Kannan:
Improving Federated Learning Personalization via Model Agnostic Meta Learning. CoRR abs/1909.12488 (2019) - no results
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