Adaptive federated optimization S Reddi, Z Charles, M Zaheer, Z Garrett, K Rush, J Konečný, S Kumar, ...
arXiv preprint arXiv:2003.00295, 2020
1666 2020 Improving federated learning personalization via model agnostic meta learning Y Jiang, J Konečný, K Rush, S Kannan
arXiv preprint arXiv:1909.12488, 2019
702 2019 Federated reconstruction: Partially local federated learning K Singhal, H Sidahmed, Z Garrett, S Wu, J Rush, S Prakash
Advances in Neural Information Processing Systems 34, 11220-11232, 2021
154 2021 Improved differential privacy for sgd via optimal private linear operators on adaptive streams S Denisov, HB McMahan, J Rush, A Smith, A Guha Thakurta
Advances in Neural Information Processing Systems 35, 5910-5924, 2022
69 * 2022 (Nearly) Dimension Independent Private ERM with AdaGrad Rates\{via Publicly Estimated Subspaces P Kairouz, MR Diaz, K Rush, A Thakurta
Conference on Learning Theory, 2717-2746, 2021
45 * 2021 Multi-epoch matrix factorization mechanisms for private machine learning CA Choquette-Choo, HB McMahan, K Rush, A Thakurta
arXiv preprint arXiv:2211.06530, 2022
40 2022 Differentially private model personalization P Jain, J Rush, A Smith, S Song, A Guha Thakurta
Advances in neural information processing systems 34, 29723-29735, 2021
39 2021 (Amplified) Banded Matrix Factorization: A unified approach to private training CA Choquette-Choo, A Ganesh, R McKenna, HB McMahan, J Rush, ...
Advances in Neural Information Processing Systems 36, 74856-74889, 2023
32 2023 Adaptive Federated Optimization (2020) S Reddi, Z Charles, M Zaheer, Z Garrett, K Rush, J Konecný, S Kumar, ...
arXiv preprint arXiv:2003.00295, 2003
25 2003 Does federated dropout actually work? G Cheng, Z Charles, Z Garrett, K Rush
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
23 2022 Gradient descent with linearly correlated noise: Theory and applications to differential privacy A Koloskova, R McKenna, Z Charles, J Rush, HB McMahan
Advances in Neural Information Processing Systems 36, 2023
17 2023 Fine-tuning large language models with user-level differential privacy Z Charles, A Ganesh, R McKenna, HB McMahan, N Mitchell, K Pillutla, ...
arXiv preprint arXiv:2407.07737, 2024
12 2024 Orthogonal Polynomials on the Circle for the Weight w Satisfying Conditions S Denisov, K Rush
Constructive Approximation 46, 285-303, 2017
11 2017 Cascade-Aware Training of Language Models C Wang, S Augenstein, K Rush, W Jitkrittum, H Narasimhan, AS Rawat, ...
arXiv preprint arXiv:2406.00060, 2024
4 2024 Iterated Vector Fields and Conservatism, with Applications to Federated Learning Z Charles, K Rush
https://arxiv.org/abs/2109.03973, 2021
4 2021 DrJAX: Scalable and Differentiable MapReduce Primitives in JAX JK Rush, Z Charles, Z Garrett, S Augenstein, NE Mitchell
2nd Workshop on Advancing Neural Network Training: Computational Efficiency …, 2024
3 * 2024 On Schur parameters in Steklov’s problem S Denisov, K Rush
Journal of Approximation Theory 215, 68-91, 2017
3 2017 Federated automatic differentiation K Rush, Z Charles, Z Garrett
Journal of Machine Learning Research 25 (357), 1-39, 2024
1 2024 Streaming DiLoCo with overlapping communication: Towards a Distributed Free Lunch A Douillard, Y Donchev, K Rush, S Kale, Z Charles, Z Garrett, G Teston, ...
arXiv preprint arXiv:2501.18512, 2025
2025 Randomized Verblunsky parameters in Steklov's problem K Rush
Journal of Mathematical Analysis and Applications 468 (2), 608-621, 2018
2018 
Zachary GarrettGoogleEmail verificata su google.com