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Keith Rush
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Adaptive federated optimization
S Reddi, Z Charles, M Zaheer, Z Garrett, K Rush, J Konečný, S Kumar, ...
arXiv preprint arXiv:2003.00295, 2020
11582020
Improving federated learning personalization via model agnostic meta learning
Y Jiang, J Konečný, K Rush, S Kannan
arXiv preprint arXiv:1909.12488, 2019
5542019
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
1102021
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
42*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
37*2021
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
292021
Multi-epoch matrix factorization mechanisms for private machine learning
CA Choquette-Choo, HB McMahan, K Rush, A Thakurta
arXiv preprint arXiv:2211.06530, 2022
172022
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
132022
Orthogonal Polynomials on the Circle for the Weight w Satisfying Conditions
S Denisov, K Rush
Constructive Approximation 46, 285-303, 2017
112017
(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, 2024
92024
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
62023
Iterated Vector Fields and Conservatism, with Applications to Federated Learning
Z Charles, K Rush
https://arxiv.org/abs/2109.03973, 2021
52021
On Schur parameters in Steklov’s problem
S Denisov, K Rush
Journal of Approximation Theory 215, 68-91, 2017
32017
Federated Automatic Differentiation
K Rush, Z Charles, Z Garrett
arXiv preprint arXiv:2301.07806, 2023
12023
FAX: Scalable and Differentiable Federated Primitives in JAX
K Rush, Z Charles, Z Garrett
arXiv preprint arXiv:2403.07128, 2024
2024
Randomized Verblunsky parameters in Steklov's problem
K Rush
Journal of Mathematical Analysis and Applications 468 (2), 608-621, 2018
2018
Orthogonal polynomials on the unit circle: Steklov problems and weight perturbations
K Rush
The University of Wisconsin-Madison, 2016
2016
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Artiklar 1–17